7915 lines
331 KiB
Rust
7915 lines
331 KiB
Rust
//! WiFi-DensePose Sensing Server
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//!
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//! Lightweight Axum server that:
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//! - Receives ESP32 CSI frames via UDP (port 5005)
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//! - Processes signals using RuVector-powered wifi-densepose-signal crate
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//! - Broadcasts sensing updates via WebSocket (ws://localhost:8765/ws/sensing)
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//! - Serves the static UI files (port 8080)
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//!
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//! Replaces both ws_server.py and the Python HTTP server.
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#![allow(dead_code)]
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mod adaptive_classifier;
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pub mod cli;
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pub mod csi;
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mod field_bridge;
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mod multistatic_bridge;
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pub mod pose;
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mod rvf_container;
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mod rvf_pipeline;
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mod tracker_bridge;
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pub mod types;
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mod vital_signs;
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// Training pipeline modules (exposed via lib.rs)
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use wifi_densepose_sensing_server::{graph_transformer, trainer, dataset, embedding};
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// ADR-116: WiFlow-v1 supervised pose inference.
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use wifi_densepose_sensing_server::wiflow_v1::{self, WiflowModel};
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use std::collections::{HashMap, VecDeque};
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use ruvector_mincut::{DynamicMinCut, MinCutBuilder};
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use std::net::SocketAddr;
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use std::path::PathBuf;
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use std::sync::Arc;
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use std::sync::{Mutex, OnceLock};
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/// Per-node adaptive baseline for `motion_energy` / `presence_score`.
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///
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/// FW reports raw values that are non-zero even in an empty room because of
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/// ambient RF noise. We compute an EWMA mean+variance over recent samples and
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/// flag presence/motion only when the current value is well above that
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/// background (z-score > 2). When the room is quiet long enough the baseline
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/// drifts up to the noise floor, so steady-state presence drops to false.
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struct BaselineTracker {
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motion_mean: f32,
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motion_var: f32,
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presence_mean: f32,
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presence_var: f32,
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samples: u32,
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/// Rolling smoothed motion (low-pass).
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motion_smooth: f32,
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/// Hysteresis: count of consecutive frames over threshold for presence on,
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/// or under threshold for presence off.
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on_count: u32,
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off_count: u32,
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presence_state: bool,
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}
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impl BaselineTracker {
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fn new() -> Self {
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Self {
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motion_mean: 0.0, motion_var: 0.01,
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presence_mean: 0.0, presence_var: 0.01,
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samples: 0,
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motion_smooth: 0.0,
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on_count: 0,
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off_count: 0,
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presence_state: false,
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}
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}
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/// Returns (is_present, motion_norm 0..1, presence_norm 0..1).
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///
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/// FW saturates `motion_score` at 1.0, so we use the derivative of
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/// `presence_score`. Empty room: deltas are mostly <0.01 with occasional
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/// noise. Human motion: produces frequent spikes of 0.05-1.0.
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///
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/// Algorithm:
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/// 1. Compute |delta_i| = |presence_i - presence_{i-1}|
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/// 2. Slide a 30-frame (~3 sec @ 10pps) window of "is_spike" bits
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/// where spike = delta > SPIKE_THRESHOLD
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/// 3. If ≥ MIN_SPIKES spikes in window → presence ON
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/// 4. If 0 spikes in window → presence OFF
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fn update(&mut self, _motion: f32, presence: f32) -> (bool, f32, f32) {
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self.samples += 1;
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let raw_delta = (presence - self.presence_mean).abs();
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self.presence_mean = presence;
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const SPIKE_THRESHOLD: f32 = 0.05;
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const MIN_SPIKES_ON: u32 = 3;
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const WINDOW: u32 = 30;
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if raw_delta > SPIKE_THRESHOLD {
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self.on_count = self.on_count.saturating_add(1);
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self.off_count = 0;
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} else {
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self.off_count = self.off_count.saturating_add(1);
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}
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// Lightweight rolling: every WINDOW frames, halve on_count so old
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// spikes decay (cheap approximation of a sliding window).
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if self.samples % WINDOW == 0 {
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self.on_count /= 2;
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}
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if self.on_count >= MIN_SPIKES_ON {
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self.presence_state = true;
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} else if self.off_count >= WINDOW {
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self.presence_state = false;
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}
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// Use smoothed delta as motion_norm for the UI's intensity bar.
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let alpha = 0.3;
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self.motion_smooth = (1.0 - alpha) * self.motion_smooth + alpha * raw_delta;
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let motion_norm = (self.motion_smooth * 5.0).clamp(0.0, 1.0);
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let presence_norm = if self.presence_state { motion_norm.max(0.3) } else { 0.0 };
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(self.presence_state, motion_norm, presence_norm)
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}
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}
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static BASELINE: OnceLock<Mutex<std::collections::HashMap<u8, BaselineTracker>>> = OnceLock::new();
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fn baseline_init() -> &'static Mutex<std::collections::HashMap<u8, BaselineTracker>> {
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BASELINE.get_or_init(|| Mutex::new(std::collections::HashMap::new()))
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}
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/// Per-node rolling RSSI window for presence detection.
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/// On the deployed TP-Link AP, empirically the standard deviation of frame
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/// RSSI over a ~5 s window separates empty/sitting/walking far more cleanly
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/// than CSI variance metrics: empty ≈ 0.35, sitting still ≈ 0.60, walking
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/// ≈ 1.0+. A human body in the channel acts as a moving absorber/reflector
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/// → RSSI flickers; an empty room has only RF background noise → flat RSSI.
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static RSSI_HIST: OnceLock<Mutex<std::collections::HashMap<u8, std::collections::VecDeque<i8>>>> = OnceLock::new();
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fn rssi_hist_init() -> &'static Mutex<std::collections::HashMap<u8, std::collections::VecDeque<i8>>> {
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RSSI_HIST.get_or_init(|| Mutex::new(std::collections::HashMap::new()))
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}
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/// Push a new RSSI sample and return rolling mean absolute delta over the
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/// last `window` samples. Returns 0.0 until we have at least `window` samples.
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/// MAD-Δ is more robust than std-dev for integer-quantised RSSI: a single
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/// 1-dB step in a quiet window inflates std but contributes minimally to
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/// the running mean of |Δ|.
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fn rssi_delta_push(node_id: u8, rssi: i8, window: usize) -> f64 {
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let mut map = rssi_hist_init().lock().unwrap();
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let q = map.entry(node_id).or_insert_with(std::collections::VecDeque::new);
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q.push_back(rssi);
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while q.len() > window { q.pop_front(); }
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if q.len() < window { return 0.0; }
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let mut sum = 0.0;
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let mut n = 0.0;
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let vals: Vec<i8> = q.iter().copied().collect();
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for i in 1..vals.len() {
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sum += (vals[i] as f64 - vals[i-1] as f64).abs();
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n += 1.0;
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}
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if n == 0.0 { 0.0 } else { sum / n }
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}
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// ── ADR-101: Raw-amplitude presence/motion classifier ──────────────────
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//
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// After ADR-100 the gain-locked baseline lets us cleanly separate the
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// EMPTY / STILL / WALK states by two cheap statistics computed over the
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// last second of broadband mean amplitude per node:
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//
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// * CV (coeff. of variation) — proxy for motion: still → 3-5 %,
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// walking → 12-30 %.
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// * mean_A vs. learned baseline — proxy for still presence: a body in
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// the AP→sensor path lowers the direct-component amplitude by 25-40 %.
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//
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// Baseline = 95th-percentile of the last ~30 s of mean_A (assumption: at
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// least one window during the past 30 s was empty/quiet). That avoids
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// hand-tuning the absolute amplitude scale per node — node 1 runs near 37,
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// node 2 near 9 in the operator's deployment; baselines adapt independently.
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/// Window length for short-term mean/CV (target ~4.5 s at 20 fps).
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/// Long enough to bridge step pauses while walking.
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const AMP_SHORT_WIN: usize = 90;
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/// Window length for long-term baseline (target ~60 s at 20 fps).
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const AMP_LONG_WIN: usize = 1200;
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/// Hysteresis hold time (in successful classifier calls) for a motion
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/// state to keep itself active after CV drops below threshold. At ~40
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/// classifier ticks/sec (both nodes combined) this gives ≈ 3 s of hold.
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const AMP_MOTION_HOLD_TICKS: u32 = 120;
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// ── ADR-102: NBVI subcarrier selection (server-side port) ──────────
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//
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// Ported from Francesco Pace's ESPectre (GPLv3). Computes a Normalized
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// Baseline Variability Index per subcarrier from a recent history of
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// amplitude vectors and picks the K with the lowest score for the CV
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// calculation in `amp_presence_override`. Lower NBVI = strong AND
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// stable subcarrier.
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//
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// NBVI(k) = α · (σ_k / μ_k²) + (1 - α) · (σ_k / μ_k), α = 0.5
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//
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// Server-side (instead of FW) avoids a second flash cycle and makes
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// the algorithm trivial to retune per deployment.
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/// Rolling buffer of per-subcarrier amplitude vectors for NBVI ranking.
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/// 600 frames ≈ 30 s at 20 fps.
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const NBVI_HISTORY_LEN: usize = 600;
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/// How many subcarriers to keep in the active set.
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const NBVI_TOP_K: usize = 12;
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/// Recompute the NBVI ranking every N classifier calls (~5 s at 40
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/// ticks/sec combined).
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const NBVI_REFRESH_TICKS: u32 = 200;
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/// Dead-zone gate: ignore subcarriers below this fraction of the
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/// median mean amplitude (guard tones + null bins).
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const NBVI_DEAD_GATE_PCT: f64 = 0.25;
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struct AmpState {
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/// Rolling short window of NBVI-subset broadband mean (used for CV).
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short: VecDeque<f64>,
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/// Rolling long window of NBVI-subset broadband mean (fallback baseline
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/// via p95 when no persistent override is loaded).
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long: VecDeque<f64>,
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/// Rolling short window of FULL broadband mean across all non-zero
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/// subcarriers. Used for the persistent-baseline drop comparison —
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/// stable across NBVI re-selection between server restarts (ADR-103).
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short_full: VecDeque<f64>,
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/// Rolling buffer of full per-subcarrier amplitude vectors.
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nbvi_history: VecDeque<Vec<f64>>,
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/// Indices of currently-selected best subcarriers (sorted by NBVI
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/// ascending). Empty until first ranking pass.
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nbvi_selected: Vec<usize>,
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/// Ticks since last NBVI recompute (for throttling).
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nbvi_ticks: u32,
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}
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/// Compute the top-K NBVI subcarrier indices over the provided history.
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/// Returns empty if the history is too short to give a stable ranking.
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///
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/// ADR-102 v2: ESPectre's Step 1 quiet-window finder is now active. We
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/// slide a fixed window across `history`, score each window by its
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/// broadband-mean coefficient of variation, and rank subcarriers using
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/// only the calmest window. This makes the selection robust to brief
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/// motion that happens during the calibration buffer (someone walks by
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/// during boot, dog enters room) — the noisy windows are ignored.
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fn nbvi_select_top_k(history: &VecDeque<Vec<f64>>, k: usize) -> Vec<usize> {
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if history.len() < AMP_SHORT_WIN { return Vec::new(); }
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let n_sub = history.front().map(|v| v.len()).unwrap_or(0);
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if n_sub == 0 { return Vec::new(); }
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// ── ESPectre Step 1: pick the quietest sub-window ────────────────
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//
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// Slide AMP_SHORT_WIN-sized window across history with stride
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// AMP_SHORT_WIN/3 (overlapping). For each window, compute the CV
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// of its broadband mean. Lowest-CV window wins. If history is small,
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// use the whole thing.
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let window_size = AMP_SHORT_WIN;
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let stride = (window_size / 3).max(1);
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let frames: Vec<&Vec<f64>> = history.iter().collect();
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let total = frames.len();
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let quiet_slice: &[&Vec<f64>] = if total <= window_size {
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&frames[..]
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} else {
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let mut best_start = 0usize;
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let mut best_cv = f64::INFINITY;
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let mut start = 0usize;
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while start + window_size <= total {
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let window = &frames[start..start + window_size];
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// Per-frame broadband mean (any valid subcarrier).
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let bb: Vec<f64> = window.iter().map(|f| {
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let mut s = 0.0; let mut c = 0;
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for &v in f.iter() { if v > 0.0 { s += v; c += 1; } }
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if c == 0 { 0.0 } else { s / c as f64 }
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}).collect();
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let mu: f64 = bb.iter().sum::<f64>() / bb.len() as f64;
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if mu > 0.0 {
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let var: f64 = bb.iter().map(|x| (x - mu).powi(2)).sum::<f64>() / bb.len() as f64;
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let cv = var.sqrt() / mu;
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if cv < best_cv { best_cv = cv; best_start = start; }
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}
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start += stride;
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}
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&frames[best_start..best_start + window_size]
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};
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// Per-subcarrier mean and std over the quietest window only.
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let n = quiet_slice.len() as f64;
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let mut means = vec![0.0_f64; n_sub];
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let mut sums = vec![0.0_f64; n_sub];
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for frame in quiet_slice.iter() {
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for k in 0..n_sub.min(frame.len()) { sums[k] += frame[k]; }
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}
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for k in 0..n_sub { means[k] = sums[k] / n; }
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let mut stds = vec![0.0_f64; n_sub];
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for frame in quiet_slice.iter() {
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for k in 0..n_sub.min(frame.len()) {
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let d = frame[k] - means[k];
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stds[k] += d * d;
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}
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}
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for k in 0..n_sub { stds[k] = (stds[k] / n).sqrt(); }
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// Dead-zone gate: keep only subcarriers above
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// NBVI_DEAD_GATE_PCT × median(mean). Guard tones (mean≈0) and weak
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// edge bins are excluded so they can't "win" with σ/μ → ∞.
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let mut sorted_means: Vec<f64> = means.iter().copied().filter(|&v| v > 0.0).collect();
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sorted_means.sort_by(|a,b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
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if sorted_means.is_empty() { return Vec::new(); }
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let median = sorted_means[sorted_means.len() / 2];
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let gate = median * NBVI_DEAD_GATE_PCT;
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// NBVI per subcarrier (α = 0.5).
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let mut scored: Vec<(usize, f64)> = (0..n_sub)
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.filter(|&k| means[k] > gate)
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.map(|k| {
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let m = means[k];
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let s = stds[k];
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let nbvi = 0.5 * (s / (m*m)) + 0.5 * (s / m);
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(k, nbvi)
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})
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.collect();
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scored.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));
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// ── ESPectre Step 3: FP-rate validation ─────────────────────────
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//
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// Don't take the raw top-K from NBVI ranking blindly. The K with
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// the lowest false-positive rate over the quiet window is the
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// winner. "FP" = times the broadband-mean CV computed from that
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// candidate subset crosses the moving threshold even though the
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// window we're evaluating was the quietest available. Smallest K
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// with FP=0 is preferred (more headroom for averaging) over a
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// bigger K that adds noisier subcarriers.
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//
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// Candidate sizes K ∈ {6, 8, 10, 12, 16, 20} clamped to scored.len().
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if scored.len() <= k {
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return scored.into_iter().map(|(k,_)| k).collect();
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}
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let ranked_indices: Vec<usize> = scored.iter().map(|(k,_)| *k).collect();
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let candidates: [usize; 6] = [6, 8, 10, 12, 16, 20];
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let fp_thresh = 0.10_f64; // matches ADR-101 D1 "present_moving" gate
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let mut best_k = k;
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let mut best_fp = usize::MAX;
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let mut best_total_nbvi = f64::INFINITY;
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for &cand_k in &candidates {
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if cand_k > ranked_indices.len() { continue; }
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let sel: &[usize] = &ranked_indices[..cand_k];
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// Compute per-frame broadband-mean across this subset.
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let bb: Vec<f64> = quiet_slice.iter().map(|f| {
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let mut s = 0.0; let mut c = 0;
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for &i in sel { if i < f.len() && f[i] > 0.0 { s += f[i]; c += 1; } }
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if c == 0 { 0.0 } else { s / c as f64 }
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}).collect();
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// Rolling CV over a sliding sub-window of ~30 samples
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// (1/3 of AMP_SHORT_WIN). Count frames where rolling CV
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// exceeds the moving gate — those would be false positives.
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let sub_win = (AMP_SHORT_WIN / 3).max(8);
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let mut fp = 0usize;
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for w_start in (0..bb.len().saturating_sub(sub_win)).step_by(sub_win / 2) {
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let w = &bb[w_start..w_start + sub_win];
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let mu: f64 = w.iter().sum::<f64>() / w.len() as f64;
|
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if mu <= 0.0 { continue; }
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let var: f64 = w.iter().map(|x| (x - mu).powi(2)).sum::<f64>() / w.len() as f64;
|
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let cv = var.sqrt() / mu;
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if cv > fp_thresh { fp += 1; }
|
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}
|
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// Sum of NBVI scores for this subset — tie-breaker.
|
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let total_nbvi: f64 = scored.iter().take(cand_k).map(|(_, n)| *n).sum();
|
||
// Pick lowest FP; on ties, smaller total NBVI.
|
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if fp < best_fp || (fp == best_fp && total_nbvi < best_total_nbvi) {
|
||
best_fp = fp;
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||
best_total_nbvi = total_nbvi;
|
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best_k = cand_k;
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}
|
||
}
|
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ranked_indices.into_iter().take(best_k).collect()
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||
}
|
||
|
||
static AMP_HIST: OnceLock<Mutex<std::collections::HashMap<u8, AmpState>>> = OnceLock::new();
|
||
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fn amp_hist_init() -> &'static Mutex<std::collections::HashMap<u8, AmpState>> {
|
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AMP_HIST.get_or_init(|| Mutex::new(std::collections::HashMap::new()))
|
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}
|
||
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/// Latest (cv, mean_short, baseline_or_None) per node, for cross-node fusion.
|
||
static AMP_LATEST: OnceLock<Mutex<std::collections::HashMap<u8, (f64, f64, Option<f64>)>>> = OnceLock::new();
|
||
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||
fn amp_latest_init() -> &'static Mutex<std::collections::HashMap<u8, (f64, f64, Option<f64>)>> {
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AMP_LATEST.get_or_init(|| Mutex::new(std::collections::HashMap::new()))
|
||
}
|
||
|
||
/// Sticky-state holdover counters so a brief CV dip (step pause) doesn't
|
||
/// flip "moving" to "absent". When CV crosses a motion threshold the
|
||
/// counter is reset to `AMP_MOTION_HOLD_TICKS`; it decrements per call
|
||
/// and the level is upgraded back up until it expires.
|
||
static AMP_HOLD: OnceLock<Mutex<(String, u32)>> = OnceLock::new();
|
||
|
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fn amp_hold_init() -> &'static Mutex<(String, u32)> {
|
||
AMP_HOLD.get_or_init(|| Mutex::new(("absent".to_string(), 0)))
|
||
}
|
||
|
||
/// ADR-103: persistent baseline override (per-node mean_amp value).
|
||
/// When set, `amp_presence_override` uses this instead of the rolling
|
||
/// 95th-percentile from AMP_HIST.long. Loaded from `data/baseline.json`
|
||
/// at startup so a fresh server boot doesn't require the "step out for
|
||
/// 60 s" calibration ritual. Empty map = fall back to rolling p95.
|
||
static AMP_BASELINE_OVERRIDE: OnceLock<Mutex<std::collections::HashMap<u8, f64>>> = OnceLock::new();
|
||
|
||
fn amp_baseline_override_init() -> &'static Mutex<std::collections::HashMap<u8, f64>> {
|
||
AMP_BASELINE_OVERRIDE.get_or_init(|| Mutex::new(std::collections::HashMap::new()))
|
||
}
|
||
|
||
/// ADR-104: per-node per-subcarrier empty-room baseline vector,
|
||
/// loaded from baseline.json `per_subcarrier_mean`. Used by the
|
||
/// classifier as a second presence channel: even when broadband
|
||
/// barely moves, comparing the current amp vector elementwise to
|
||
/// this baseline catches off-axis bodies that only modulate a
|
||
/// handful of subcarriers.
|
||
static AMP_BASELINE_PER_SUB: OnceLock<Mutex<std::collections::HashMap<u8, Vec<f64>>>> = OnceLock::new();
|
||
fn amp_baseline_per_sub_init() -> &'static Mutex<std::collections::HashMap<u8, Vec<f64>>> {
|
||
AMP_BASELINE_PER_SUB.get_or_init(|| Mutex::new(std::collections::HashMap::new()))
|
||
}
|
||
|
||
/// ADR-104 phase-domain: per-node `(phase_mean_rad, phase_var)` vectors
|
||
/// loaded from baseline.json `per_subcarrier_phase_mean` +
|
||
/// `per_subcarrier_phase_var`. Optional — present only when the
|
||
/// recorder captured complex CSI. A high `var` (close to 1.0) on a
|
||
/// subcarrier means the baseline phase was unstable across the
|
||
/// recording window, so that subcarrier's per-tick phase delta is
|
||
/// unreliable and the server discards it from the phase drift score.
|
||
static PHASE_BASELINE_PER_SUB: OnceLock<Mutex<std::collections::HashMap<u8, (Vec<f64>, Vec<f64>)>>> = OnceLock::new();
|
||
fn phase_baseline_per_sub_init()
|
||
-> &'static Mutex<std::collections::HashMap<u8, (Vec<f64>, Vec<f64>)>>
|
||
{
|
||
PHASE_BASELINE_PER_SUB.get_or_init(|| Mutex::new(std::collections::HashMap::new()))
|
||
}
|
||
|
||
/// ADR-104 phase-domain: per-node "phase drift" score in `[0, 1]`,
|
||
/// updated each tick. 0 = current phases match the baseline; 1 = π
|
||
/// rad away (maximally far on the unit circle). Computed only when a
|
||
/// phase baseline exposes ≥ 16 usable subcarriers (var < threshold).
|
||
static PHASE_DRIFT: OnceLock<Mutex<std::collections::HashMap<u8, f64>>> = OnceLock::new();
|
||
fn phase_drift_init() -> &'static Mutex<std::collections::HashMap<u8, f64>> {
|
||
PHASE_DRIFT.get_or_init(|| Mutex::new(std::collections::HashMap::new()))
|
||
}
|
||
/// Discard subcarriers whose baseline phase variance exceeds this.
|
||
/// 0.30 corresponds to mean resultant length R ≈ 0.70 — phases were
|
||
/// reasonably clustered during baseline capture. Tunable, conservative.
|
||
const PHASE_BASELINE_VAR_MAX: f64 = 0.30;
|
||
/// Minimum usable subcarriers required to emit a phase drift score.
|
||
/// Below this the score is too noisy to trust and we return None.
|
||
const PHASE_DRIFT_MIN_USABLE: usize = 16;
|
||
|
||
/// ADR-104: per-node "spectral drift" score = mean |Δ amp / baseline|
|
||
/// across subcarriers, computed against AMP_BASELINE_PER_SUB. Updated
|
||
/// every classifier tick; read by amp_node_level / amp_classify_from_latest
|
||
/// as a second `present_still` trigger.
|
||
static AMP_DRIFT: OnceLock<Mutex<std::collections::HashMap<u8, f64>>> = OnceLock::new();
|
||
fn amp_drift_init() -> &'static Mutex<std::collections::HashMap<u8, f64>> {
|
||
AMP_DRIFT.get_or_init(|| Mutex::new(std::collections::HashMap::new()))
|
||
}
|
||
fn amp_drift_for_node(node_id: u8) -> f64 {
|
||
let m = amp_drift_init().lock().unwrap();
|
||
m.get(&node_id).copied().unwrap_or(0.0)
|
||
}
|
||
fn amp_drift_max() -> f64 {
|
||
let m = amp_drift_init().lock().unwrap();
|
||
m.values().copied().fold(0.0_f64, f64::max)
|
||
}
|
||
/// ADR-104: spectral-drift threshold — fraction (e.g. 0.10 = 10 %)
|
||
/// that average per-subcarrier deviation must exceed to flag presence.
|
||
/// Empirical; matches the broadband ratio trigger (drop ≥ 25 %, drift ≥ 10 %).
|
||
const AMP_DRIFT_PRESENCE_THRESH: f64 = 0.10;
|
||
|
||
/// ADR-107: timestamp of the most recent baseline load/write. Auto
|
||
/// recalibrator uses this to enforce a cool-down between writes; the
|
||
/// REST endpoint reports it so the UI can show "calibrated X min ago".
|
||
static BASELINE_LAST_WRITTEN: OnceLock<Mutex<std::time::SystemTime>> = OnceLock::new();
|
||
fn baseline_last_written_init() -> &'static Mutex<std::time::SystemTime> {
|
||
BASELINE_LAST_WRITTEN.get_or_init(|| Mutex::new(std::time::UNIX_EPOCH))
|
||
}
|
||
|
||
/// ADR-107: in-progress calibration state for the REST endpoint.
|
||
/// 'idle' | 'running' | 'complete' | 'error: …'
|
||
static BASELINE_CALIBRATION_STATUS: OnceLock<Mutex<String>> = OnceLock::new();
|
||
fn baseline_calib_status_init() -> &'static Mutex<String> {
|
||
BASELINE_CALIBRATION_STATUS.get_or_init(|| Mutex::new("idle".to_string()))
|
||
}
|
||
|
||
/// Load persistent baseline from JSON file. Tolerant: missing file or
|
||
/// parse errors are non-fatal (server falls back to rolling p95).
|
||
fn load_baseline_file(path: &str) {
|
||
let s = match std::fs::read_to_string(path) {
|
||
Ok(s) => s,
|
||
Err(_) => {
|
||
info!("baseline: no file at {path} — using rolling p95 (60-s warmup)");
|
||
return;
|
||
}
|
||
};
|
||
let v: serde_json::Value = match serde_json::from_str(&s) {
|
||
Ok(v) => v,
|
||
Err(e) => {
|
||
warn!("baseline: parse error at {path}: {e}");
|
||
return;
|
||
}
|
||
};
|
||
let nodes = match v.get("nodes").and_then(|n| n.as_object()) {
|
||
Some(n) => n,
|
||
None => { warn!("baseline: no .nodes object in {path}"); return; }
|
||
};
|
||
let mut loaded: Vec<(u8, f64)> = Vec::new();
|
||
let mut loaded_cv: Vec<(u8, f64)> = Vec::new();
|
||
for (k, node) in nodes {
|
||
let id: u8 = match k.parse() { Ok(i) => i, Err(_) => continue };
|
||
// ADR-103 v2 schema (preferred): full_broadband_p95 / full_broadband_mean
|
||
// — stable across NBVI re-selection between restarts. Falls back to
|
||
// legacy NBVI-subset p95/mean if a v1 baseline.json was loaded.
|
||
let full_p95 = node.get("full_broadband_p95").and_then(|v| v.as_f64());
|
||
let full_mean = node.get("full_broadband_mean").and_then(|v| v.as_f64());
|
||
let nbvi_p95 = node.get("p95_amp").and_then(|v| v.as_f64());
|
||
let nbvi_mean = node.get("mean_amp").and_then(|v| v.as_f64());
|
||
let baseline = [full_p95, full_mean, nbvi_p95, nbvi_mean]
|
||
.into_iter().flatten().find(|v| *v > 0.0);
|
||
let Some(b) = baseline else { continue };
|
||
loaded.push((id, b));
|
||
|
||
// ADR-103 v2: per-node baseline CV for universal threshold
|
||
// normalization (Pace's Problem #3). Accept either schema field.
|
||
let cv_pct = node.get("full_broadband_cv_pct")
|
||
.or_else(|| node.get("cv_pct"))
|
||
.and_then(|v| v.as_f64())
|
||
.unwrap_or(0.0);
|
||
if cv_pct > 0.0 {
|
||
loaded_cv.push((id, cv_pct / 100.0));
|
||
}
|
||
|
||
// ADR-104: per-subcarrier baseline vector for off-axis
|
||
// presence detection. Optional; only present if the
|
||
// recording script wrote `per_subcarrier_mean`.
|
||
if let Some(arr) = node.get("per_subcarrier_mean").and_then(|v| v.as_array()) {
|
||
let vec: Vec<f64> = arr.iter().filter_map(|v| v.as_f64()).collect();
|
||
if vec.len() >= 16 {
|
||
let mut o = amp_baseline_per_sub_init().lock().unwrap();
|
||
o.insert(id, vec);
|
||
}
|
||
}
|
||
// ADR-104 phase-domain: load per-subcarrier circular mean +
|
||
// variance vectors. Optional; only present when the recorder
|
||
// captured complex CSI (ADR-106). Lengths must match — if they
|
||
// don't we drop the phase baseline rather than silently mixing
|
||
// bad data into the drift score.
|
||
let p_mean = node.get("per_subcarrier_phase_mean").and_then(|v| v.as_array());
|
||
let p_var = node.get("per_subcarrier_phase_var").and_then(|v| v.as_array());
|
||
if let (Some(m), Some(v)) = (p_mean, p_var) {
|
||
let means: Vec<f64> = m.iter().filter_map(|x| x.as_f64()).collect();
|
||
let vars: Vec<f64> = v.iter().filter_map(|x| x.as_f64()).collect();
|
||
if means.len() == vars.len() && means.len() >= 16 {
|
||
let mut o = phase_baseline_per_sub_init().lock().unwrap();
|
||
o.insert(id, (means, vars));
|
||
}
|
||
}
|
||
}
|
||
if loaded.is_empty() {
|
||
warn!("baseline: {path} parsed but no usable per-node entries");
|
||
return;
|
||
}
|
||
{
|
||
let mut o = amp_baseline_override_init().lock().unwrap();
|
||
for (id, b) in &loaded { o.insert(*id, *b); }
|
||
}
|
||
{
|
||
let mut o = amp_baseline_cv_init().lock().unwrap();
|
||
for (id, cv) in &loaded_cv { o.insert(*id, *cv); }
|
||
}
|
||
// ADR-107: track when the baseline file was last loaded/written so
|
||
// the auto-recalibrator and REST endpoint can stage cool-downs.
|
||
{
|
||
let mut t = baseline_last_written_init().lock().unwrap();
|
||
*t = std::time::SystemTime::now();
|
||
}
|
||
let summary: Vec<String> = loaded.iter().map(|(id, b)| format!("node{id}={b:.2}")).collect();
|
||
let cv_summary: Vec<String> = loaded_cv.iter()
|
||
.map(|(id, cv)| format!("node{id}_cv={:.2}%", cv * 100.0)).collect();
|
||
info!("baseline: loaded {} node overrides from {} ({}; {})",
|
||
loaded.len(), path, summary.join(", "),
|
||
if cv_summary.is_empty() { "no CV normalization".to_string() } else { cv_summary.join(", ") });
|
||
}
|
||
|
||
/// ADR-104 phase-domain: update PHASE_DRIFT for a node from the
|
||
/// current per-subcarrier phases. Compares current phase to baseline
|
||
/// using circular distance, averaged over subcarriers whose baseline
|
||
/// variance is below `PHASE_BASELINE_VAR_MAX` (unstable subcarriers
|
||
/// would dominate with noise). Output is normalised to `[0, 1]`
|
||
/// where 0 = phases match baseline exactly and 1 = π rad apart.
|
||
///
|
||
/// No-op if a phase baseline isn't loaded for this node, or if fewer
|
||
/// than `PHASE_DRIFT_MIN_USABLE` subcarriers pass the variance gate.
|
||
/// Honesty contract: better to surface no score than a noisy one.
|
||
fn phase_drift_update(node_id: u8, phases: &[f64]) {
|
||
if phases.is_empty() {
|
||
return;
|
||
}
|
||
let base = phase_baseline_per_sub_init().lock().unwrap();
|
||
let (b_mean, b_var) = match base.get(&node_id) {
|
||
Some(t) => (t.0.clone(), t.1.clone()),
|
||
None => return,
|
||
};
|
||
drop(base);
|
||
let n = b_mean.len().min(phases.len());
|
||
if n == 0 { return; }
|
||
let mut sum = 0.0_f64;
|
||
let mut usable: usize = 0;
|
||
for k in 0..n {
|
||
if b_var[k] > PHASE_BASELINE_VAR_MAX { continue; }
|
||
// Circular distance via the imaginary part of e^(i Δφ),
|
||
// taken |.| and normalised by π. Equivalent to
|
||
// |atan2(sin Δ, cos Δ)| / π but cheaper.
|
||
let delta = phases[k] - b_mean[k];
|
||
let s = delta.sin();
|
||
let c = delta.cos();
|
||
let d = s.atan2(c).abs() / std::f64::consts::PI;
|
||
sum += d;
|
||
usable += 1;
|
||
}
|
||
if usable < PHASE_DRIFT_MIN_USABLE { return; }
|
||
let score = (sum / usable as f64).clamp(0.0, 1.0);
|
||
let mut m = phase_drift_init().lock().unwrap();
|
||
m.insert(node_id, score);
|
||
}
|
||
|
||
/// Classify motion/presence for one node from the raw amplitude vector.
|
||
///
|
||
/// Returns `(motion_level, presence, confidence)` where confidence is the
|
||
/// raw CV value (so the UI can show it during tuning). Returns `None` for
|
||
/// the first ~1.5 s while the short window fills.
|
||
fn amp_presence_override(node_id: u8, amplitudes: &[f64]) -> Option<(String, bool, f64)> {
|
||
if amplitudes.is_empty() {
|
||
return None;
|
||
}
|
||
let mut map = amp_hist_init().lock().unwrap();
|
||
let st = map.entry(node_id).or_insert_with(|| AmpState {
|
||
short: VecDeque::with_capacity(AMP_SHORT_WIN),
|
||
long: VecDeque::with_capacity(AMP_LONG_WIN),
|
||
short_full: VecDeque::with_capacity(AMP_SHORT_WIN),
|
||
nbvi_history: VecDeque::with_capacity(NBVI_HISTORY_LEN),
|
||
nbvi_selected: Vec::new(),
|
||
nbvi_ticks: 0,
|
||
});
|
||
|
||
// ADR-103 v2: compute FULL broadband mean (all non-zero subcarriers)
|
||
// for the persistent baseline drop check. Stable across NBVI
|
||
// re-selection between server restarts. NBVI subset is still used
|
||
// for CV (motion sensitivity).
|
||
let full_mean: f64 = {
|
||
let mut s = 0.0; let mut c = 0;
|
||
for &v in amplitudes.iter() { if v > 0.0 { s += v; c += 1; } }
|
||
if c == 0 { 0.0 } else { s / c as f64 }
|
||
};
|
||
st.short_full.push_back(full_mean);
|
||
while st.short_full.len() > AMP_SHORT_WIN { st.short_full.pop_front(); }
|
||
|
||
// Push current frame into NBVI history for ranking.
|
||
st.nbvi_history.push_back(amplitudes.to_vec());
|
||
while st.nbvi_history.len() > NBVI_HISTORY_LEN { st.nbvi_history.pop_front(); }
|
||
|
||
// Refresh NBVI selection periodically.
|
||
st.nbvi_ticks = st.nbvi_ticks.saturating_add(1);
|
||
if st.nbvi_selected.is_empty() || st.nbvi_ticks >= NBVI_REFRESH_TICKS {
|
||
st.nbvi_selected = nbvi_select_top_k(&st.nbvi_history, NBVI_TOP_K);
|
||
st.nbvi_ticks = 0;
|
||
}
|
||
|
||
// Compute broadband_mean. Use the NBVI-selected subset when
|
||
// available — it tracks body modulation much more cleanly than the
|
||
// full vector. Falls back to all non-zero subcarriers during
|
||
// warmup when NBVI hasn't ranked yet.
|
||
let broadband_mean: f64 = if !st.nbvi_selected.is_empty() {
|
||
let mut sum = 0.0; let mut cnt = 0;
|
||
for &k in &st.nbvi_selected {
|
||
if k < amplitudes.len() && amplitudes[k] > 0.0 {
|
||
sum += amplitudes[k]; cnt += 1;
|
||
}
|
||
}
|
||
if cnt == 0 { return None; }
|
||
sum / cnt as f64
|
||
} else {
|
||
let valid: Vec<f64> = amplitudes.iter().copied().filter(|&v| v > 0.0).collect();
|
||
if valid.is_empty() { return None; }
|
||
valid.iter().sum::<f64>() / valid.len() as f64
|
||
};
|
||
|
||
st.short.push_back(broadband_mean);
|
||
while st.short.len() > AMP_SHORT_WIN { st.short.pop_front(); }
|
||
st.long.push_back(broadband_mean);
|
||
while st.long.len() > AMP_LONG_WIN { st.long.pop_front(); }
|
||
|
||
if st.short.len() < AMP_SHORT_WIN {
|
||
return None;
|
||
}
|
||
|
||
// Short-window mean + CV.
|
||
let n = st.short.len() as f64;
|
||
let sum: f64 = st.short.iter().sum();
|
||
let mean_short = sum / n;
|
||
let var: f64 = st.short.iter().map(|x| (x - mean_short).powi(2)).sum::<f64>() / n;
|
||
let cv = if mean_short > 0.0 { var.sqrt() / mean_short } else { 0.0 };
|
||
|
||
// Baseline:
|
||
// 1. Persistent override (ADR-103) loaded from data/baseline.json
|
||
// at boot. Represents the FULL-broadband mean of the empty
|
||
// room. Stable across NBVI re-selection between restarts.
|
||
// 2. Fall back to the rolling 95th percentile of the long FULL
|
||
// window when no override is present.
|
||
//
|
||
// A body in the channel attenuates amplitude, so the baseline
|
||
// (= empty-room amplitude) sits at the upper end of recent history.
|
||
let baseline = {
|
||
let ovr = amp_baseline_override_init().lock().unwrap();
|
||
if let Some(&fixed) = ovr.get(&node_id) {
|
||
Some(fixed)
|
||
} else if st.long.len() >= AMP_SHORT_WIN * 3 {
|
||
// Rolling fallback uses NBVI-subset (long) for backwards
|
||
// compatibility with the legacy non-baseline mode.
|
||
let mut sorted: Vec<f64> = st.long.iter().copied().collect();
|
||
sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
|
||
let idx = ((sorted.len() as f64) * 0.95) as usize;
|
||
Some(sorted[idx.min(sorted.len() - 1)])
|
||
} else {
|
||
None
|
||
}
|
||
};
|
||
|
||
// mean_for_baseline_check: when override is loaded → use FULL
|
||
// broadband (stable across NBVI changes). Otherwise use NBVI subset
|
||
// (matches the legacy rolling baseline). Same source on both sides
|
||
// of the comparison.
|
||
let use_full = {
|
||
let ovr = amp_baseline_override_init().lock().unwrap();
|
||
ovr.contains_key(&node_id)
|
||
};
|
||
let mean_for_baseline = if use_full && !st.short_full.is_empty() {
|
||
st.short_full.iter().sum::<f64>() / st.short_full.len() as f64
|
||
} else {
|
||
mean_short
|
||
};
|
||
|
||
// ── ADR-104: per-subcarrier delta as off-axis presence channel ──
|
||
//
|
||
// If a per-subcarrier baseline vector is loaded for this node,
|
||
// compare current per-subcarrier mean (over the NBVI history's
|
||
// last AMP_SHORT_WIN frames) against it. Sum of |delta / baseline|
|
||
// for subcarriers with baseline > 1.0 → "spectral drift score".
|
||
// High drift = body modulated the channel even if broadband mean
|
||
// didn't change much (i.e. operator is in the room but off-axis).
|
||
//
|
||
// Stashed as a side-channel into AMP_LATEST_DRIFT; the per-node
|
||
// classifier reads it as a third trigger for `present_still`.
|
||
let drift = {
|
||
let per_sub_map = amp_baseline_per_sub_init().lock().unwrap();
|
||
match per_sub_map.get(&node_id) {
|
||
Some(base_vec) if st.nbvi_history.len() >= AMP_SHORT_WIN => {
|
||
// Per-sub mean from recent frames.
|
||
let recent: Vec<&Vec<f64>> = st.nbvi_history.iter()
|
||
.rev().take(AMP_SHORT_WIN).collect();
|
||
let n_sub = base_vec.len().min(recent.first().map_or(0, |v| v.len()));
|
||
if n_sub < 8 { 0.0 } else {
|
||
let mut score = 0.0;
|
||
let mut cnt = 0;
|
||
for k in 0..n_sub {
|
||
let b = base_vec[k];
|
||
if b <= 1.0 { continue; }
|
||
let mut sum = 0.0; let mut c = 0;
|
||
for f in &recent { if k < f.len() && f[k] > 0.0 { sum += f[k]; c += 1; } }
|
||
if c == 0 { continue; }
|
||
let cur = sum / c as f64;
|
||
score += (cur - b).abs() / b;
|
||
cnt += 1;
|
||
}
|
||
if cnt == 0 { 0.0 } else { score / cnt as f64 }
|
||
}
|
||
}
|
||
_ => 0.0,
|
||
}
|
||
};
|
||
{
|
||
let mut d = amp_drift_init().lock().unwrap();
|
||
d.insert(node_id, drift);
|
||
}
|
||
|
||
// Stash this node's contribution for cross-node fusion.
|
||
{
|
||
let mut latest = amp_latest_init().lock().unwrap();
|
||
latest.insert(node_id, (cv, mean_for_baseline, baseline));
|
||
}
|
||
|
||
amp_classify_from_latest()
|
||
}
|
||
|
||
/// Classify a single node's recent state — used both inside the global
|
||
/// fusion and from `build_node_features` so the UI can show per-node
|
||
/// labels. No hysteresis is applied here; that's a global property.
|
||
fn amp_node_level(cv: f64, mean_short: f64, baseline: Option<f64>) -> (&'static str, bool) {
|
||
// ADR-102 + Pace's Problem #3: thresholds are *universal* — applied
|
||
// to the **normalized** motion score (cv / baseline_cv). One
|
||
// threshold set works in any room.
|
||
let bcv = amp_baseline_cv_for_node();
|
||
let norm_cv = if bcv > 0.0 { cv / bcv } else { cv };
|
||
if norm_cv >= 6.0 {
|
||
return ("active", true);
|
||
}
|
||
if norm_cv >= 3.0 {
|
||
return ("present_moving", true);
|
||
}
|
||
// ADR-101 broadband-drop trigger.
|
||
if matches!(baseline, Some(b) if b > 0.0 && (mean_short / b) < 0.75) {
|
||
return ("present_still", true);
|
||
}
|
||
// ADR-104: off-axis presence — per-subcarrier drift channel.
|
||
// Triggers when body is in the room but off the AP→sensor line,
|
||
// so broadband mean barely shifts.
|
||
// (Caller doesn't pass per-node id here; we read MAX drift via
|
||
// amp_drift_max(). Per-node decisions inside snapshot read their
|
||
// own value separately.)
|
||
if amp_drift_max() >= AMP_DRIFT_PRESENCE_THRESH {
|
||
return ("present_still", true);
|
||
}
|
||
("absent", false)
|
||
}
|
||
|
||
/// Average baseline CV across nodes that have a calibration loaded.
|
||
/// Returns 0.0 if no calibration is loaded — caller falls back to raw CV.
|
||
fn amp_baseline_cv_for_node() -> f64 {
|
||
let cvs = amp_baseline_cv_init().lock().unwrap();
|
||
if cvs.is_empty() { return 0.0; }
|
||
cvs.values().sum::<f64>() / cvs.len() as f64
|
||
}
|
||
|
||
/// Per-node baseline CV (decimal, not %) loaded from data/baseline.json.
|
||
/// Used to normalize the runtime CV so threshold comparison is universal.
|
||
static AMP_BASELINE_CV: OnceLock<Mutex<std::collections::HashMap<u8, f64>>> = OnceLock::new();
|
||
fn amp_baseline_cv_init() -> &'static Mutex<std::collections::HashMap<u8, f64>> {
|
||
AMP_BASELINE_CV.get_or_init(|| Mutex::new(std::collections::HashMap::new()))
|
||
}
|
||
|
||
/// Per-node snapshot exposed to `build_node_features`.
|
||
fn amp_node_snapshot(node_id: u8) -> Option<(String, bool, f64)> {
|
||
let latest = amp_latest_init().lock().unwrap();
|
||
let (cv, mean_short, baseline) = latest.get(&node_id).copied()?;
|
||
// amp_node_level uses amp_drift_max() (cross-node) for the drift
|
||
// trigger. For per-node display we want this node's own drift,
|
||
// so override after the base classify.
|
||
let (lvl0, pres0) = amp_node_level(cv, mean_short, baseline);
|
||
let my_drift = amp_drift_for_node(node_id);
|
||
let (lvl, pres) =
|
||
if matches!(lvl0, "active" | "present_moving") {
|
||
(lvl0, pres0)
|
||
} else if my_drift >= AMP_DRIFT_PRESENCE_THRESH {
|
||
// ADR-104: this specific node sees per-subcarrier drift
|
||
// (body in its line-of-sight to the AP), regardless of what
|
||
// the cross-node MAX-drift heuristic said.
|
||
("present_still", true)
|
||
} else if matches!(baseline, Some(b) if b > 0.0 && (mean_short / b) < 0.75) {
|
||
("present_still", true)
|
||
} else {
|
||
("absent", false)
|
||
};
|
||
Some((lvl.to_string(), pres, cv))
|
||
}
|
||
|
||
/// Per-node (mean_short, baseline_or_None) for diagnostics. Lets the UI
|
||
/// surface "baseline learned" vs "current" so the operator can see why
|
||
/// `present_still` is/isn't firing.
|
||
pub(crate) fn amp_node_diag(node_id: u8) -> Option<(f64, Option<f64>)> {
|
||
let latest = amp_latest_init().lock().unwrap();
|
||
latest.get(&node_id).map(|(_, mean_short, baseline)| (*mean_short, *baseline))
|
||
}
|
||
|
||
/// Read-only classifier: returns `(level, presence, confidence)` based on
|
||
/// whatever `amp_presence_override` has stashed for the active nodes.
|
||
/// Returns None until at least one node has reported.
|
||
///
|
||
/// Used by SensingUpdate-producing paths that don't carry raw amplitudes
|
||
/// (feature_state / vitals packets). Lets those paths inherit the same
|
||
/// classification that the raw-CSI path already computed, instead of
|
||
/// emitting a stale or different label.
|
||
fn amp_classify_from_latest() -> Option<(String, bool, f64)> {
|
||
// ── Cross-node fusion ────────────────────────────────────────────
|
||
//
|
||
// We use MAX CV across nodes for the motion gate (any node sees
|
||
// movement → trust it; body modulates only the line-of-sight
|
||
// path it crosses, the other node may stay clean). To compensate
|
||
// for one node's natural noise, the moving threshold is raised
|
||
// empirically. Baseline drop still flags "present_still" when both
|
||
// nodes are quiet.
|
||
//
|
||
// ADR-101 thresholds (per-node MAX, deployment-tuned for low-AGC
|
||
// ESP32-S3 with the operator's TP-Link geometry):
|
||
// max_cv >= 30 % → active
|
||
// max_cv >= 15 % → present_moving
|
||
// any baseline drop → present_still
|
||
// otherwise → absent
|
||
//
|
||
// Sticky hold (AMP_MOTION_HOLD_TICKS calls ≈ 3 s) prevents flicker
|
||
// when CV briefly dips below threshold (e.g. step pause).
|
||
let snapshot: Vec<(f64, f64, Option<f64>)> = {
|
||
let latest = amp_latest_init().lock().unwrap();
|
||
latest.values().copied().collect()
|
||
};
|
||
if snapshot.is_empty() {
|
||
return None;
|
||
}
|
||
let max_cv = snapshot.iter().map(|(c, _, _)| *c).fold(0.0_f64, f64::max);
|
||
let any_baseline_drop = snapshot.iter().any(|(_, m, b)| {
|
||
matches!(b, Some(bv) if *bv > 0.0 && (*m / *bv) < 0.75)
|
||
});
|
||
|
||
// ADR-103 v2: normalize max_cv by loaded baseline CV (Pace's
|
||
// Problem #3 universal threshold). Falls back to absolute gates
|
||
// when no calibration is loaded — keeps backwards compatibility.
|
||
let bcv = amp_baseline_cv_for_node();
|
||
let norm_max_cv = if bcv > 0.0 { max_cv / bcv } else { max_cv };
|
||
let (gate_active, gate_moving) = if bcv > 0.0 { (6.0, 3.0) } else { (0.22, 0.10) };
|
||
// ADR-104: cross-node spectral drift triggers `present_still`
|
||
// even when broadband drop didn't fire — off-axis body presence.
|
||
let any_drift = amp_drift_max() >= AMP_DRIFT_PRESENCE_THRESH;
|
||
let candidate = if norm_max_cv >= gate_active {
|
||
"active"
|
||
} else if norm_max_cv >= gate_moving {
|
||
"present_moving"
|
||
} else if any_baseline_drop || any_drift {
|
||
"present_still"
|
||
} else {
|
||
"absent"
|
||
};
|
||
|
||
// Sticky hysteresis on motion states: once "moving"/"active", keep
|
||
// that label until the hold timer expires.
|
||
let level: String;
|
||
let presence: bool;
|
||
{
|
||
let mut hold = amp_hold_init().lock().unwrap();
|
||
let candidate_is_motion = matches!(candidate, "present_moving" | "active");
|
||
if candidate_is_motion {
|
||
// Refresh hold to full.
|
||
hold.0 = candidate.to_string();
|
||
hold.1 = AMP_MOTION_HOLD_TICKS;
|
||
level = candidate.to_string();
|
||
} else if hold.1 > 0 && matches!(hold.0.as_str(), "present_moving" | "active") {
|
||
// Within hold window — keep prior motion label even though
|
||
// current tick says quiet.
|
||
hold.1 -= 1;
|
||
level = hold.0.clone();
|
||
} else {
|
||
// No motion + no hold → either present_still (baseline drop)
|
||
// or absent.
|
||
hold.0 = candidate.to_string();
|
||
hold.1 = 0;
|
||
level = candidate.to_string();
|
||
}
|
||
presence = !matches!(level.as_str(), "absent");
|
||
}
|
||
|
||
// Confidence carries max CV — strongest motion signal across the
|
||
// swarm — so the UI can surface live noise during tuning.
|
||
Some((level, presence, max_cv))
|
||
}
|
||
|
||
/// Override (motion_level, presence) from rolling RSSI MAD-Δ.
|
||
/// Returns None until window has filled.
|
||
#[allow(dead_code)] // superseded by amp_presence_override (ADR-101); kept for reference
|
||
fn rssi_presence_override(node_id: u8, rssi: i8) -> Option<(String, bool, f64)> {
|
||
let d = rssi_delta_push(node_id, rssi, 120); // ~10 sec @ 12 Hz
|
||
if d == 0.0 { return None; }
|
||
// Empirical thresholds for the room01/room02 TP-Link deployment.
|
||
// Empty room: mean |Δrssi| stays near 0 because RSSI sits at one int8 value
|
||
// for many frames. Human in channel: 0.3-0.7. Walking: 0.7+.
|
||
let (level, presence) = if d < 0.20 {
|
||
("absent", false)
|
||
} else if d < 0.55 {
|
||
("present_still", true)
|
||
} else if d < 1.10 {
|
||
("present_moving", true)
|
||
} else {
|
||
("active", true)
|
||
};
|
||
// TEMP: surface the raw d via confidence so we can tune thresholds.
|
||
let conf = d;
|
||
Some((level.to_string(), presence, conf))
|
||
}
|
||
|
||
use std::time::Duration;
|
||
|
||
use axum::{
|
||
extract::{
|
||
ws::{Message, WebSocket, WebSocketUpgrade},
|
||
Path,
|
||
State,
|
||
},
|
||
response::{Html, IntoResponse, Json},
|
||
routing::{delete, get, post},
|
||
Router,
|
||
};
|
||
use clap::Parser;
|
||
|
||
use serde::{Deserialize, Serialize};
|
||
use tokio::net::UdpSocket;
|
||
use tokio::sync::{broadcast, RwLock};
|
||
use tower_http::services::ServeDir;
|
||
use tower_http::set_header::SetResponseHeaderLayer;
|
||
use axum::http::HeaderValue;
|
||
use tracing::{info, warn, debug, error};
|
||
|
||
use rvf_container::{RvfBuilder, RvfContainerInfo, RvfReader, VitalSignConfig};
|
||
use rvf_pipeline::ProgressiveLoader;
|
||
use vital_signs::{VitalSignDetector, VitalSigns};
|
||
|
||
// ADR-022 Phase 3: Multi-BSSID pipeline integration
|
||
use wifi_densepose_wifiscan::{
|
||
BssidRegistry, WindowsWifiPipeline,
|
||
};
|
||
use wifi_densepose_wifiscan::parse_netsh_output as parse_netsh_bssid_output;
|
||
|
||
// Accuracy sprint: Kalman tracker, multistatic fusion, field model
|
||
use wifi_densepose_signal::ruvsense::pose_tracker::PoseTracker;
|
||
use wifi_densepose_signal::ruvsense::multistatic::{MultistaticFuser, MultistaticConfig};
|
||
use wifi_densepose_signal::ruvsense::field_model::{FieldModel, CalibrationStatus};
|
||
|
||
// ── CLI ──────────────────────────────────────────────────────────────────────
|
||
|
||
#[derive(Parser, Debug)]
|
||
#[command(name = "sensing-server", about = "WiFi-DensePose sensing server")]
|
||
struct Args {
|
||
/// HTTP port for UI and REST API
|
||
#[arg(long, default_value = "8080")]
|
||
http_port: u16,
|
||
|
||
/// WebSocket port for sensing stream
|
||
#[arg(long, default_value = "8765")]
|
||
ws_port: u16,
|
||
|
||
/// UDP port for ESP32 CSI frames
|
||
#[arg(long, default_value = "5005")]
|
||
udp_port: u16,
|
||
|
||
/// ADR-106: keepalive packets/sec sent back to each sensor to drive
|
||
/// CSI callback rate (no FW change required). 0 disables.
|
||
#[arg(long, default_value = "20")]
|
||
csi_keepalive_pps: u32,
|
||
|
||
/// ADR-107: auto-recalibrate baseline in background when the room
|
||
/// has been `absent` and quiet for N seconds. Set to 0 to disable.
|
||
/// Default 1800 = 30 min — long enough that someone occasionally
|
||
/// in the room won't trigger spurious recalibrations.
|
||
#[arg(long, default_value = "1800")]
|
||
auto_recalibrate_quiet_sec: f64,
|
||
|
||
/// ADR-107: cool-down (seconds) between auto-recalibration writes.
|
||
/// Default 3600 = at most once per hour.
|
||
#[arg(long, default_value = "3600")]
|
||
auto_recalibrate_min_age_sec: f64,
|
||
|
||
/// ADR-104: warn when the on-disk baseline is older than this many
|
||
/// seconds AND the per-subcarrier drift channel has been firing while
|
||
/// the classifier reports `absent`. Default 14400 = 4 h. Set to 0 to
|
||
/// disable the watcher. Independent from --auto-recalibrate-*: that
|
||
/// path needs a quiet room, this one flags channels that *can't* get
|
||
/// quiet (operator working in the room while the AP physically moved).
|
||
#[arg(long, default_value = "14400")]
|
||
baseline_stale_age_sec: f64,
|
||
|
||
/// ADR-104: cool-down (seconds) between baseline-stale warnings.
|
||
/// Default 3600 = at most once per hour.
|
||
#[arg(long, default_value = "3600")]
|
||
baseline_stale_warn_cooldown_sec: f64,
|
||
|
||
/// ADR-113: baseline profile selector.
|
||
/// * `single` (default): load `RUVIEW_BASELINE_FILE` or
|
||
/// `data/baseline.json`. Backwards-compatible behaviour.
|
||
/// * `auto`: pick `data/baseline.day.json` or
|
||
/// `data/baseline.night.json` based on local hour
|
||
/// (day = 07:00–20:59, night = 21:00–06:59). Hot-reloads on
|
||
/// transitions. Falls back to single-baseline on either file
|
||
/// missing.
|
||
/// * `day` / `night`: force one of the profile files; no
|
||
/// auto-switching.
|
||
/// The "single" path is unchanged so existing deployments don't
|
||
/// need to migrate.
|
||
#[arg(long, default_value = "single")]
|
||
baseline_profile: String,
|
||
|
||
/// Path to UI static files
|
||
#[arg(long, default_value = "../../ui")]
|
||
ui_path: PathBuf,
|
||
|
||
/// Tick interval in milliseconds (default 100 ms = 10 fps for smooth pose animation)
|
||
#[arg(long, default_value = "100")]
|
||
tick_ms: u64,
|
||
|
||
/// Bind address (default 127.0.0.1; set to 0.0.0.0 for network access)
|
||
#[arg(long, default_value = "127.0.0.1", env = "SENSING_BIND_ADDR")]
|
||
bind_addr: String,
|
||
|
||
/// Data source: auto, wifi, esp32, simulate
|
||
#[arg(long, default_value = "auto")]
|
||
source: String,
|
||
|
||
/// Run vital sign detection benchmark (1000 frames) and exit
|
||
#[arg(long)]
|
||
benchmark: bool,
|
||
|
||
/// Load model config from an RVF container at startup
|
||
#[arg(long, value_name = "PATH")]
|
||
load_rvf: Option<PathBuf>,
|
||
|
||
/// Save current model state as an RVF container on shutdown
|
||
#[arg(long, value_name = "PATH")]
|
||
save_rvf: Option<PathBuf>,
|
||
|
||
/// Load a trained .rvf model for inference
|
||
#[arg(long, value_name = "PATH")]
|
||
model: Option<PathBuf>,
|
||
|
||
/// Enable progressive loading (Layer A instant start)
|
||
#[arg(long)]
|
||
progressive: bool,
|
||
|
||
/// Export an RVF container package and exit (no server)
|
||
#[arg(long, value_name = "PATH")]
|
||
export_rvf: Option<PathBuf>,
|
||
|
||
/// Run training mode (train a model and exit)
|
||
#[arg(long)]
|
||
train: bool,
|
||
|
||
/// Path to dataset directory (MM-Fi or Wi-Pose)
|
||
#[arg(long, value_name = "PATH")]
|
||
dataset: Option<PathBuf>,
|
||
|
||
/// Dataset type: "mmfi" or "wipose"
|
||
#[arg(long, value_name = "TYPE", default_value = "mmfi")]
|
||
dataset_type: String,
|
||
|
||
/// Number of training epochs
|
||
#[arg(long, default_value = "100")]
|
||
epochs: usize,
|
||
|
||
/// Directory for training checkpoints
|
||
#[arg(long, value_name = "DIR")]
|
||
checkpoint_dir: Option<PathBuf>,
|
||
|
||
/// Run self-supervised contrastive pretraining (ADR-024)
|
||
#[arg(long)]
|
||
pretrain: bool,
|
||
|
||
/// Number of pretraining epochs (default 50)
|
||
#[arg(long, default_value = "50")]
|
||
pretrain_epochs: usize,
|
||
|
||
/// Extract embeddings mode: load model and extract CSI embeddings
|
||
#[arg(long)]
|
||
embed: bool,
|
||
|
||
/// Build fingerprint index from embeddings (env|activity|temporal|person)
|
||
#[arg(long, value_name = "TYPE")]
|
||
build_index: Option<String>,
|
||
|
||
/// Node positions for multistatic fusion (format: "x,y,z;x,y,z;...")
|
||
#[arg(long, env = "SENSING_NODE_POSITIONS")]
|
||
node_positions: Option<String>,
|
||
|
||
/// Start field model calibration on boot (empty room required)
|
||
#[arg(long)]
|
||
calibrate: bool,
|
||
|
||
/// ADR-116: Load WiFlow-v1 supervised pose model JSON
|
||
/// (`v2/data/models/ruview/wiflow-v1/wiflow-v1.json`). When loaded,
|
||
/// `pose_estimation` flips to true and `/api/v1/pose/*` returns
|
||
/// real 17-keypoint COCO skeletons instead of empty arrays.
|
||
/// Independent from `--model` (RVF container) and `--load-rvf`.
|
||
#[arg(long, value_name = "PATH")]
|
||
wiflow_model: Option<PathBuf>,
|
||
}
|
||
|
||
/// ADR-116: globally-shared WiFlow-v1 model. Loaded once at startup if
|
||
/// `--wiflow-model` was passed; consumed by `run_wiflow_inference()` on
|
||
/// every tick. None ⇒ pose endpoints stay gated per ADR-105.
|
||
static WIFLOW_MODEL: OnceLock<Option<WiflowModel>> = OnceLock::new();
|
||
|
||
// ── Data types ───────────────────────────────────────────────────────────────
|
||
|
||
/// ADR-018 ESP32 CSI binary frame header (20 bytes)
|
||
#[derive(Debug, Clone)]
|
||
#[allow(dead_code)]
|
||
struct Esp32Frame {
|
||
magic: u32,
|
||
node_id: u8,
|
||
n_antennas: u8,
|
||
n_subcarriers: u8,
|
||
freq_mhz: u16,
|
||
sequence: u32,
|
||
rssi: i8,
|
||
noise_floor: i8,
|
||
amplitudes: Vec<f64>,
|
||
phases: Vec<f64>,
|
||
/// ADR-106 trailing field — sensor µs timestamp from
|
||
/// `info->rx_ctrl.timestamp`. Monotonic µs since FW boot.
|
||
/// `None` for old FW that doesn't carry it.
|
||
sensor_timestamp_us: Option<u32>,
|
||
}
|
||
|
||
/// Sensing update broadcast to WebSocket clients
|
||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||
struct SensingUpdate {
|
||
#[serde(rename = "type")]
|
||
msg_type: String,
|
||
timestamp: f64,
|
||
source: String,
|
||
tick: u64,
|
||
nodes: Vec<NodeInfo>,
|
||
features: FeatureInfo,
|
||
classification: ClassificationInfo,
|
||
signal_field: SignalField,
|
||
/// Vital sign estimates (breathing rate, heart rate, confidence).
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
vital_signs: Option<VitalSigns>,
|
||
// ── ADR-022 Phase 3: Enhanced multi-BSSID pipeline fields ──
|
||
/// Enhanced motion estimate from multi-BSSID pipeline.
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
enhanced_motion: Option<serde_json::Value>,
|
||
/// Enhanced breathing estimate from multi-BSSID pipeline.
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
enhanced_breathing: Option<serde_json::Value>,
|
||
/// Posture classification from BSSID fingerprint matching.
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
posture: Option<String>,
|
||
/// Signal quality score from multi-BSSID quality gate [0.0, 1.0].
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
signal_quality_score: Option<f64>,
|
||
/// Quality gate verdict: "Permit", "Warn", or "Deny".
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
quality_verdict: Option<String>,
|
||
/// Number of BSSIDs used in the enhanced sensing cycle.
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
bssid_count: Option<usize>,
|
||
// ── ADR-023 Phase 7-8: Model inference fields ──
|
||
/// Pose keypoints when a trained model is loaded (x, y, z, confidence).
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
pose_keypoints: Option<Vec<[f64; 4]>>,
|
||
/// Model status when a trained model is loaded.
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
model_status: Option<serde_json::Value>,
|
||
// ── Multi-person detection (issue #97) ──
|
||
/// Detected persons from WiFi sensing (multi-person support).
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
persons: Option<Vec<PersonDetection>>,
|
||
/// Estimated person count from CSI feature heuristics (1-3 for single ESP32).
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
estimated_persons: Option<usize>,
|
||
/// Per-node feature breakdown for multi-node deployments.
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
node_features: Option<Vec<PerNodeFeatureInfo>>,
|
||
}
|
||
|
||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||
struct NodeInfo {
|
||
node_id: u8,
|
||
rssi_dbm: f64,
|
||
position: [f64; 3],
|
||
/// Per-subcarrier amplitude = sqrt(I² + Q²) — primary CSI signal.
|
||
amplitude: Vec<f64>,
|
||
/// Per-subcarrier phase in radians = atan2(Q, I). ADR-106: now
|
||
/// exposed alongside amplitude so downstream consumers (vital-
|
||
/// signs FFT on phase, pose estimation, ML training) have the
|
||
/// full complex CSI. Empty when the carrying packet was a
|
||
/// feature_state (no raw CSI).
|
||
#[serde(default, skip_serializing_if = "Vec::is_empty")]
|
||
phases: Vec<f64>,
|
||
subcarrier_count: usize,
|
||
/// Number of receive antennas reported by the WiFi driver
|
||
/// (ESP32-S3 typically 1). 0 when the source packet didn't carry it.
|
||
#[serde(default, skip_serializing_if = "is_zero_u8")]
|
||
n_antennas: u8,
|
||
/// Receiver noise floor in dBm. 0 means "not reported".
|
||
#[serde(default, skip_serializing_if = "is_zero_i8")]
|
||
noise_floor_dbm: i8,
|
||
/// Per-frame µs timestamp from the receiving sensor. Lets the
|
||
/// server / model align frames across nodes when computing FFTs
|
||
/// or cross-correlations. 0 means "not available".
|
||
#[serde(default, skip_serializing_if = "is_zero_u64")]
|
||
timestamp_us: u64,
|
||
}
|
||
|
||
fn is_zero_u8(v: &u8) -> bool { *v == 0 }
|
||
fn is_zero_i8(v: &i8) -> bool { *v == 0 }
|
||
fn is_zero_u64(v: &u64) -> bool { *v == 0 }
|
||
|
||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||
struct FeatureInfo {
|
||
mean_rssi: f64,
|
||
variance: f64,
|
||
motion_band_power: f64,
|
||
breathing_band_power: f64,
|
||
dominant_freq_hz: f64,
|
||
change_points: usize,
|
||
spectral_power: f64,
|
||
}
|
||
|
||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||
struct ClassificationInfo {
|
||
motion_level: String,
|
||
presence: bool,
|
||
confidence: f64,
|
||
}
|
||
|
||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||
struct SignalField {
|
||
grid_size: [usize; 3],
|
||
values: Vec<f64>,
|
||
}
|
||
|
||
/// WiFi-derived pose keypoint (17 COCO keypoints)
|
||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||
struct PoseKeypoint {
|
||
name: String,
|
||
x: f64,
|
||
y: f64,
|
||
z: f64,
|
||
confidence: f64,
|
||
}
|
||
|
||
/// Person detection from WiFi sensing
|
||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||
struct PersonDetection {
|
||
id: u32,
|
||
confidence: f64,
|
||
keypoints: Vec<PoseKeypoint>,
|
||
bbox: BoundingBox,
|
||
zone: String,
|
||
}
|
||
|
||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||
struct BoundingBox {
|
||
x: f64,
|
||
y: f64,
|
||
width: f64,
|
||
height: f64,
|
||
}
|
||
|
||
/// Per-node sensing state for multi-node deployments (issue #249).
|
||
/// Each ESP32 node gets its own frame history, smoothing buffers, and vital
|
||
/// sign detector so that data from different nodes is never mixed.
|
||
struct NodeState {
|
||
pub(crate) frame_history: VecDeque<Vec<f64>>,
|
||
smoothed_person_score: f64,
|
||
pub(crate) prev_person_count: usize,
|
||
smoothed_motion: f64,
|
||
current_motion_level: String,
|
||
debounce_counter: u32,
|
||
debounce_candidate: String,
|
||
baseline_motion: f64,
|
||
baseline_frames: u64,
|
||
smoothed_hr: f64,
|
||
smoothed_br: f64,
|
||
smoothed_hr_conf: f64,
|
||
smoothed_br_conf: f64,
|
||
hr_buffer: VecDeque<f64>,
|
||
br_buffer: VecDeque<f64>,
|
||
rssi_history: VecDeque<f64>,
|
||
vital_detector: VitalSignDetector,
|
||
latest_vitals: VitalSigns,
|
||
pub(crate) last_frame_time: Option<std::time::Instant>,
|
||
edge_vitals: Option<Esp32VitalsPacket>,
|
||
/// Latest extracted features for cross-node fusion.
|
||
latest_features: Option<FeatureInfo>,
|
||
/// ADR-106: latest per-subcarrier phases (radians, atan2(Q,I)) and
|
||
/// noise floor + sensor µs timestamp from the most recent raw CSI
|
||
/// frame. Surfaced in `NodeInfo` so downstream consumers
|
||
/// (vital-signs FFT on phase, future ML model) get the full
|
||
/// complex CSI without re-routing through `frame_history` which
|
||
/// is amplitude-only.
|
||
latest_phases: Option<Vec<f64>>,
|
||
latest_noise_floor: i8,
|
||
latest_timestamp_us: u64,
|
||
latest_n_antennas: u8,
|
||
// ── RuVector Phase 2: Temporal smoothing & coherence gating ──
|
||
/// Previous frame's smoothed keypoint positions for EMA temporal smoothing.
|
||
prev_keypoints: Option<Vec<[f64; 3]>>,
|
||
/// Rolling buffer of motion_energy values for coherence scoring (last 20 frames).
|
||
motion_energy_history: VecDeque<f64>,
|
||
/// Coherence score [0.0, 1.0]: low variance in motion_energy = high coherence.
|
||
coherence_score: f64,
|
||
/// ADR-084 Pass 3 cluster-Pi novelty sensor — per-node sketch bank of
|
||
/// recent CSI feature vectors. Populated by `update_novelty` on each
|
||
/// frame; left `None` to disable the sensor on a per-node basis.
|
||
feature_history: Option<wifi_densepose_signal::ruvsense::longitudinal::EmbeddingHistory>,
|
||
/// Most recent novelty score in [0.0, 1.0] (0 = exact-match in bank,
|
||
/// 1 = no overlap). Consumed by the model-wake gate downstream.
|
||
pub(crate) last_novelty_score: Option<f32>,
|
||
}
|
||
|
||
/// Default EMA alpha for temporal keypoint smoothing (RuVector Phase 2).
|
||
/// Lower = smoother (more history, less jitter). 0.15 balances responsiveness
|
||
/// with stability for WiFi CSI where per-frame noise is high.
|
||
const TEMPORAL_EMA_ALPHA_DEFAULT: f64 = 0.15;
|
||
/// Reduced EMA alpha when coherence is low (trust measurements less).
|
||
const TEMPORAL_EMA_ALPHA_LOW_COHERENCE: f64 = 0.05;
|
||
/// Coherence threshold below which we reduce EMA alpha.
|
||
const COHERENCE_LOW_THRESHOLD: f64 = 0.3;
|
||
/// Maximum allowed bone-length change ratio between frames (20%).
|
||
const MAX_BONE_CHANGE_RATIO: f64 = 0.20;
|
||
/// Number of motion_energy frames to track for coherence scoring.
|
||
const COHERENCE_WINDOW: usize = 20;
|
||
/// ADR-084 Pass 3 — per-node novelty sketch dimension (56 subcarriers,
|
||
/// the dominant ESP32-S3 capture configuration).
|
||
const NOVELTY_VECTOR_DIM: usize = 56;
|
||
/// ADR-084 Pass 3 — number of past sketches retained per-node for
|
||
/// novelty comparison. 64 frames ≈ 6.4 s at 10 Hz.
|
||
const NOVELTY_HISTORY_CAPACITY: usize = 64;
|
||
/// ADR-084 Pass 3 — feature-vector schema version. Bump on changes to
|
||
/// subcarrier ordering / normalisation so banks reject stale data.
|
||
const NOVELTY_SKETCH_VERSION: u16 = 1;
|
||
|
||
impl NodeState {
|
||
pub(crate) fn new() -> Self {
|
||
Self {
|
||
frame_history: VecDeque::new(),
|
||
smoothed_person_score: 0.0,
|
||
prev_person_count: 0,
|
||
smoothed_motion: 0.0,
|
||
current_motion_level: "absent".to_string(),
|
||
debounce_counter: 0,
|
||
debounce_candidate: "absent".to_string(),
|
||
baseline_motion: 0.0,
|
||
baseline_frames: 0,
|
||
smoothed_hr: 0.0,
|
||
smoothed_br: 0.0,
|
||
smoothed_hr_conf: 0.0,
|
||
smoothed_br_conf: 0.0,
|
||
hr_buffer: VecDeque::with_capacity(8),
|
||
br_buffer: VecDeque::with_capacity(8),
|
||
rssi_history: VecDeque::new(),
|
||
vital_detector: VitalSignDetector::new(10.0),
|
||
latest_vitals: VitalSigns::default(),
|
||
last_frame_time: None,
|
||
edge_vitals: None,
|
||
latest_features: None,
|
||
latest_phases: None,
|
||
latest_noise_floor: 0,
|
||
latest_timestamp_us: 0,
|
||
latest_n_antennas: 0,
|
||
prev_keypoints: None,
|
||
motion_energy_history: VecDeque::with_capacity(COHERENCE_WINDOW),
|
||
coherence_score: 1.0, // assume stable initially
|
||
feature_history: Some(
|
||
wifi_densepose_signal::ruvsense::longitudinal::EmbeddingHistory::with_sketch(
|
||
NOVELTY_VECTOR_DIM,
|
||
NOVELTY_HISTORY_CAPACITY,
|
||
NOVELTY_SKETCH_VERSION,
|
||
),
|
||
),
|
||
last_novelty_score: None,
|
||
}
|
||
}
|
||
|
||
/// ADR-084 cluster-Pi novelty step. Truncates / zero-pads the
|
||
/// incoming amplitude vector to `NOVELTY_VECTOR_DIM`, scores its
|
||
/// novelty against the per-node bank, then inserts it. The novelty
|
||
/// score is computed *before* the insert so a frame doesn't see
|
||
/// itself in the bank.
|
||
pub(crate) fn update_novelty(&mut self, amplitudes: &[f64]) {
|
||
let history = match &mut self.feature_history {
|
||
Some(h) => h,
|
||
None => return,
|
||
};
|
||
let mut feature: Vec<f32> = amplitudes
|
||
.iter()
|
||
.take(NOVELTY_VECTOR_DIM)
|
||
.map(|&v| v as f32)
|
||
.collect();
|
||
feature.resize(NOVELTY_VECTOR_DIM, 0.0);
|
||
|
||
// Score before insert so a query doesn't see itself.
|
||
self.last_novelty_score = history.novelty(&feature);
|
||
|
||
let _ = history.push(
|
||
wifi_densepose_signal::ruvsense::longitudinal::EmbeddingEntry {
|
||
person_id: 0,
|
||
day_us: 0,
|
||
embedding: feature,
|
||
},
|
||
);
|
||
}
|
||
|
||
/// Update the coherence score from the latest motion_energy value.
|
||
///
|
||
/// Coherence is computed as 1.0 / (1.0 + running_variance) so that
|
||
/// low motion-energy variance maps to high coherence ([0, 1]).
|
||
fn update_coherence(&mut self, motion_energy: f64) {
|
||
if self.motion_energy_history.len() >= COHERENCE_WINDOW {
|
||
self.motion_energy_history.pop_front();
|
||
}
|
||
self.motion_energy_history.push_back(motion_energy);
|
||
|
||
let n = self.motion_energy_history.len();
|
||
if n < 2 {
|
||
self.coherence_score = 1.0;
|
||
return;
|
||
}
|
||
|
||
let mean: f64 = self.motion_energy_history.iter().sum::<f64>() / n as f64;
|
||
let variance: f64 = self.motion_energy_history.iter()
|
||
.map(|v| (v - mean) * (v - mean))
|
||
.sum::<f64>() / (n - 1) as f64;
|
||
|
||
// Map variance to [0, 1] coherence: higher variance = lower coherence.
|
||
self.coherence_score = (1.0 / (1.0 + variance)).clamp(0.0, 1.0);
|
||
}
|
||
|
||
/// Choose the EMA alpha based on current coherence score.
|
||
fn ema_alpha(&self) -> f64 {
|
||
if self.coherence_score < COHERENCE_LOW_THRESHOLD {
|
||
TEMPORAL_EMA_ALPHA_LOW_COHERENCE
|
||
} else {
|
||
TEMPORAL_EMA_ALPHA_DEFAULT
|
||
}
|
||
}
|
||
}
|
||
|
||
/// Per-node feature info for WebSocket broadcasts (multi-node support).
|
||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||
struct PerNodeFeatureInfo {
|
||
node_id: u8,
|
||
features: FeatureInfo,
|
||
classification: ClassificationInfo,
|
||
rssi_dbm: f64,
|
||
last_seen_ms: u64,
|
||
frame_rate_hz: f64,
|
||
stale: bool,
|
||
/// ADR-084 Pass 3 cluster-Pi novelty score in `[0.0, 1.0]`.
|
||
/// `0.0` = exact-match-in-bank, `1.0` = no overlap with recent
|
||
/// per-node frame history. `None` until the first
|
||
/// `update_novelty()` call. Consumers (model-wake gate, anomaly
|
||
/// emit, UI heatmap) read this to decide whether to escalate.
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
novelty_score: Option<f32>,
|
||
/// ADR-104 per-subcarrier drift score = mean |Δ amp / baseline|
|
||
/// over subcarriers with baseline > 1.0. `None` if no per-sub
|
||
/// baseline is loaded for this node (legacy v1 baseline.json or no
|
||
/// `per_subcarrier_mean` field). Operators read this in raw.html
|
||
/// to see the off-axis presence channel firing in real time.
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
drift_score: Option<f64>,
|
||
/// ADR-104 phase-domain drift score in `[0, 1]`. 0 = current
|
||
/// per-subcarrier phases match the captured baseline; 1 = phases
|
||
/// are π rad apart on every usable subcarrier. `None` until either
|
||
/// (a) no per-subcarrier phase baseline is loaded or (b) fewer
|
||
/// than `PHASE_DRIFT_MIN_USABLE` subcarriers pass the baseline-
|
||
/// variance gate. More sensitive than amplitude drift to sub-mm
|
||
/// chest-wall motion (vital signs).
|
||
#[serde(skip_serializing_if = "Option::is_none")]
|
||
phase_drift_score: Option<f64>,
|
||
}
|
||
|
||
/// Build a per-node feature snapshot for the WebSocket envelope.
|
||
///
|
||
/// ADR-084 Pass 3.6 — exposes `last_novelty_score` from each
|
||
/// `NodeState` to the WebSocket consumer. Returns `None` when the
|
||
/// node map is empty (no live ESP32 frames have been ingested yet),
|
||
/// so the existing `node_features: None` semantics on cold-start are
|
||
/// preserved.
|
||
///
|
||
/// Stale flag uses 5-second threshold matching `ESP32_OFFLINE_TIMEOUT`.
|
||
fn build_node_features(
|
||
node_states: &std::collections::HashMap<u8, NodeState>,
|
||
now: std::time::Instant,
|
||
) -> Option<Vec<PerNodeFeatureInfo>> {
|
||
if node_states.is_empty() {
|
||
return None;
|
||
}
|
||
let entries: Vec<PerNodeFeatureInfo> = node_states
|
||
.iter()
|
||
.map(|(&node_id, ns)| {
|
||
let last_seen_ms = ns
|
||
.last_frame_time
|
||
.map(|t| now.saturating_duration_since(t).as_millis() as u64)
|
||
.unwrap_or(u64::MAX);
|
||
let stale = ns
|
||
.last_frame_time
|
||
.map(|t| now.saturating_duration_since(t) > ESP32_OFFLINE_TIMEOUT)
|
||
.unwrap_or(true);
|
||
let features = ns.latest_features.clone().unwrap_or(FeatureInfo {
|
||
mean_rssi: 0.0,
|
||
variance: 0.0,
|
||
motion_band_power: 0.0,
|
||
breathing_band_power: 0.0,
|
||
dominant_freq_hz: 0.0,
|
||
change_points: 0,
|
||
spectral_power: 0.0,
|
||
});
|
||
// ADR-101: prefer the raw-amplitude classifier per node when
|
||
// available. Falls back to legacy current_motion_level for
|
||
// older paths that haven't reported amplitudes yet.
|
||
let classification = match amp_node_snapshot(node_id) {
|
||
Some((level, presence, conf)) => ClassificationInfo {
|
||
motion_level: level,
|
||
presence,
|
||
confidence: conf,
|
||
},
|
||
None => ClassificationInfo {
|
||
motion_level: ns.current_motion_level.clone(),
|
||
presence: !matches!(ns.current_motion_level.as_str(), "absent"),
|
||
confidence: ns.smoothed_person_score.clamp(0.0, 1.0),
|
||
},
|
||
};
|
||
// ADR-104: surface the per-subcarrier drift score for this
|
||
// node (None if no per-sub baseline is loaded — distinguishes
|
||
// "channel unknown" from "channel known and stable at 0.0").
|
||
let drift_score = {
|
||
let m = amp_drift_init().lock().unwrap();
|
||
m.get(&node_id).copied()
|
||
};
|
||
// ADR-104 phase-domain drift (None when no phase baseline
|
||
// loaded or too few usable subcarriers).
|
||
let phase_drift_score = {
|
||
let m = phase_drift_init().lock().unwrap();
|
||
m.get(&node_id).copied()
|
||
};
|
||
PerNodeFeatureInfo {
|
||
node_id,
|
||
features,
|
||
classification,
|
||
rssi_dbm: ns.rssi_history.back().copied().unwrap_or(0.0),
|
||
last_seen_ms,
|
||
frame_rate_hz: 0.0, // Computed elsewhere; not yet plumbed here.
|
||
stale,
|
||
novelty_score: ns.last_novelty_score,
|
||
drift_score,
|
||
phase_drift_score,
|
||
}
|
||
})
|
||
.collect();
|
||
Some(entries)
|
||
}
|
||
|
||
/// Shared application state
|
||
struct AppStateInner {
|
||
latest_update: Option<SensingUpdate>,
|
||
rssi_history: VecDeque<f64>,
|
||
/// Circular buffer of recent CSI amplitude vectors for temporal analysis.
|
||
/// Each entry is the full subcarrier amplitude vector for one frame.
|
||
/// Capacity: FRAME_HISTORY_CAPACITY frames.
|
||
frame_history: VecDeque<Vec<f64>>,
|
||
/// ADR-120: rolling buffer of the last WINDOW_FRAMES (=20) feature
|
||
/// vectors from `features_from_runtime`. Used at classify time to
|
||
/// feed the WindowedMlp inside the adaptive model. Pushed each tick
|
||
/// before the broadcast emit. Cold start: classify_window falls back
|
||
/// to frame-level until the buffer fills.
|
||
feature_window: VecDeque<[f64; adaptive_classifier::N_FEATURES_PUB]>,
|
||
tick: u64,
|
||
source: String,
|
||
/// Instant of the last ESP32 UDP frame received (for offline detection).
|
||
last_esp32_frame: Option<std::time::Instant>,
|
||
tx: broadcast::Sender<String>,
|
||
// ADR-099 D2/D3/D4: real-time CSI introspection tap. Per-frame state +
|
||
// a parallel broadcast topic (`/ws/introspection`) running alongside
|
||
// (not replacing) the window-aggregated `tx` / `/ws/sensing` pipeline.
|
||
intro: wifi_densepose_sensing_server::introspection::IntrospectionState,
|
||
intro_tx: broadcast::Sender<String>,
|
||
total_detections: u64,
|
||
start_time: std::time::Instant,
|
||
/// Vital sign detector (processes CSI frames to estimate HR/RR).
|
||
vital_detector: VitalSignDetector,
|
||
/// Most recent vital sign reading for the REST endpoint.
|
||
latest_vitals: VitalSigns,
|
||
/// RVF container info if a model was loaded via `--load-rvf`.
|
||
rvf_info: Option<RvfContainerInfo>,
|
||
/// Path to save RVF container on shutdown (set via `--save-rvf`).
|
||
save_rvf_path: Option<PathBuf>,
|
||
/// Progressive loader for a trained model (set via `--model`).
|
||
progressive_loader: Option<ProgressiveLoader>,
|
||
/// Active SONA profile name.
|
||
active_sona_profile: Option<String>,
|
||
/// Whether a trained model is loaded.
|
||
model_loaded: bool,
|
||
/// Smoothed person count (EMA) for hysteresis — prevents frame-to-frame jumping.
|
||
smoothed_person_score: f64,
|
||
/// Previous person count for hysteresis (asymmetric up/down thresholds).
|
||
prev_person_count: usize,
|
||
// ── Motion smoothing & adaptive baseline (ADR-047 tuning) ────────────
|
||
/// EMA-smoothed motion score (alpha ~0.15 for ~10 FPS → ~1s time constant).
|
||
smoothed_motion: f64,
|
||
/// Current classification state for hysteresis debounce.
|
||
current_motion_level: String,
|
||
/// How many consecutive frames the *raw* classification has agreed with a
|
||
/// *candidate* new level. State only changes after DEBOUNCE_FRAMES.
|
||
debounce_counter: u32,
|
||
/// The candidate motion level that the debounce counter is tracking.
|
||
debounce_candidate: String,
|
||
/// Adaptive baseline: EMA of motion score when room is "quiet" (low motion).
|
||
/// Subtracted from raw score so slow environmental drift doesn't inflate readings.
|
||
baseline_motion: f64,
|
||
/// Number of frames processed so far (for baseline warm-up).
|
||
baseline_frames: u64,
|
||
// ── Vital signs smoothing ────────────────────────────────────────────
|
||
/// EMA-smoothed heart rate (BPM).
|
||
smoothed_hr: f64,
|
||
/// EMA-smoothed breathing rate (BPM).
|
||
smoothed_br: f64,
|
||
/// EMA-smoothed HR confidence.
|
||
smoothed_hr_conf: f64,
|
||
/// EMA-smoothed BR confidence.
|
||
smoothed_br_conf: f64,
|
||
/// Median filter buffer for HR (last N raw values for outlier rejection).
|
||
hr_buffer: VecDeque<f64>,
|
||
/// Median filter buffer for BR.
|
||
br_buffer: VecDeque<f64>,
|
||
/// ADR-039: Latest edge vitals packet from ESP32.
|
||
edge_vitals: Option<Esp32VitalsPacket>,
|
||
/// ADR-040: Latest WASM output packet from ESP32.
|
||
latest_wasm_events: Option<WasmOutputPacket>,
|
||
// ── Model management fields ─────────────────────────────────────────────
|
||
/// Discovered RVF model files from `data/models/`.
|
||
discovered_models: Vec<serde_json::Value>,
|
||
/// ID of the currently loaded model, if any.
|
||
active_model_id: Option<String>,
|
||
// ── Recording fields ────────────────────────────────────────────────────
|
||
/// Metadata for recorded CSI data files.
|
||
recordings: Vec<serde_json::Value>,
|
||
/// Whether CSI recording is currently in progress.
|
||
recording_active: bool,
|
||
/// When the current recording started.
|
||
recording_start_time: Option<std::time::Instant>,
|
||
/// ID of the current recording (used for filename).
|
||
recording_current_id: Option<String>,
|
||
/// Shutdown signal for the recording writer task.
|
||
recording_stop_tx: Option<tokio::sync::watch::Sender<bool>>,
|
||
// ── Training fields ─────────────────────────────────────────────────────
|
||
/// Training status: "idle", "running", "completed", "failed".
|
||
training_status: String,
|
||
/// Training configuration, if any.
|
||
training_config: Option<serde_json::Value>,
|
||
// ── Adaptive classifier (environment-tuned) ──────────────────────────
|
||
/// Trained adaptive model (loaded from data/adaptive_model.json or trained at runtime).
|
||
adaptive_model: Option<adaptive_classifier::AdaptiveModel>,
|
||
// ── Per-node state (issue #249) ─────────────────────────────────────
|
||
/// Per-node sensing state for multi-node deployments.
|
||
/// Keyed by `node_id` from the ESP32 frame header.
|
||
node_states: HashMap<u8, NodeState>,
|
||
// ── Accuracy sprint: Kalman tracker, multistatic fusion, eigenvalue counting ──
|
||
/// Global Kalman-based pose tracker for stable person IDs and smoothed keypoints.
|
||
pose_tracker: PoseTracker,
|
||
/// Instant of last tracker update (for computing dt).
|
||
last_tracker_instant: Option<std::time::Instant>,
|
||
/// Attention-weighted multi-node CSI fusion engine.
|
||
multistatic_fuser: MultistaticFuser,
|
||
/// SVD-based room field model for eigenvalue person counting (None until calibration).
|
||
field_model: Option<FieldModel>,
|
||
}
|
||
|
||
/// If no ESP32 frame arrives within this duration, source reverts to offline.
|
||
const ESP32_OFFLINE_TIMEOUT: std::time::Duration = std::time::Duration::from_secs(5);
|
||
|
||
impl AppStateInner {
|
||
/// Return the effective data source, accounting for ESP32 frame timeout.
|
||
/// If the source is "esp32" but no frame has arrived in 5 seconds, returns
|
||
/// "esp32:offline" so the UI can distinguish active vs stale connections.
|
||
/// Person count: eigenvalue-based if field model is calibrated, else heuristic.
|
||
/// Uses global frame_history if populated, otherwise the freshest per-node history.
|
||
fn person_count(&self) -> usize {
|
||
match self.field_model.as_ref() {
|
||
Some(fm) => {
|
||
// Prefer global frame_history (populated by wifi/simulate paths).
|
||
// Fall back to freshest per-node history (populated by ESP32 paths).
|
||
let history = if !self.frame_history.is_empty() {
|
||
&self.frame_history
|
||
} else {
|
||
// Find the node with the most recent frame
|
||
self.node_states.values()
|
||
.filter(|ns| !ns.frame_history.is_empty())
|
||
.max_by_key(|ns| ns.last_frame_time)
|
||
.map(|ns| &ns.frame_history)
|
||
.unwrap_or(&self.frame_history)
|
||
};
|
||
field_bridge::occupancy_or_fallback(
|
||
fm, history, self.smoothed_person_score, self.prev_person_count,
|
||
)
|
||
}
|
||
None => score_to_person_count(self.smoothed_person_score, self.prev_person_count),
|
||
}
|
||
}
|
||
|
||
fn effective_source(&self) -> String {
|
||
if self.source == "esp32" {
|
||
if let Some(last) = self.last_esp32_frame {
|
||
if last.elapsed() > ESP32_OFFLINE_TIMEOUT {
|
||
return "esp32:offline".to_string();
|
||
}
|
||
}
|
||
}
|
||
self.source.clone()
|
||
}
|
||
}
|
||
|
||
/// Number of frames retained in `frame_history` for temporal analysis.
|
||
/// At 500 ms ticks this covers ~50 seconds; at 100 ms ticks ~10 seconds.
|
||
const FRAME_HISTORY_CAPACITY: usize = 100;
|
||
|
||
type SharedState = Arc<RwLock<AppStateInner>>;
|
||
|
||
// ── ESP32 Edge Vitals Packet (ADR-039, magic 0xC511_0002) ────────────────────
|
||
|
||
/// Decoded vitals packet from ESP32 edge processing pipeline.
|
||
#[derive(Debug, Clone, Serialize)]
|
||
struct Esp32VitalsPacket {
|
||
node_id: u8,
|
||
presence: bool,
|
||
fall_detected: bool,
|
||
motion: bool,
|
||
breathing_rate_bpm: f64,
|
||
heartrate_bpm: f64,
|
||
rssi: i8,
|
||
n_persons: u8,
|
||
motion_energy: f32,
|
||
presence_score: f32,
|
||
timestamp_ms: u32,
|
||
}
|
||
|
||
/// Parse a 60-byte ADR-081 feature_state packet (magic 0xC511_0006).
|
||
/// Converts into the local Esp32VitalsPacket so the existing vitals
|
||
/// pipeline handles real ESP32 nodes uniformly.
|
||
fn parse_rv_feature_state(buf: &[u8]) -> Option<Esp32VitalsPacket> {
|
||
if buf.len() < 60 { return None; }
|
||
let magic = u32::from_le_bytes([buf[0], buf[1], buf[2], buf[3]]);
|
||
if magic != 0xC511_0006 { return None; }
|
||
|
||
let node_id = buf[4];
|
||
let ts_us = u64::from_le_bytes([
|
||
buf[8], buf[9], buf[10], buf[11], buf[12], buf[13], buf[14], buf[15],
|
||
]);
|
||
let motion_score = f32::from_le_bytes([buf[16], buf[17], buf[18], buf[19]]);
|
||
let presence_score = f32::from_le_bytes([buf[20], buf[21], buf[22], buf[23]]);
|
||
let respiration_bpm = f32::from_le_bytes([buf[24], buf[25], buf[26], buf[27]]);
|
||
let heartbeat_bpm = f32::from_le_bytes([buf[32], buf[33], buf[34], buf[35]]);
|
||
let quality_flags = u16::from_le_bytes([buf[52], buf[53]]);
|
||
// ADR-100 D3: FW now ships median RSSI in byte 54 (was `reserved`). Zero
|
||
// means "not yet measured" — keep the historical -50 fallback in that
|
||
// case so the UI doesn't show a misleading 0 dBm.
|
||
let rssi_byte = buf[54] as i8;
|
||
let rssi: i8 = if rssi_byte == 0 { -50 } else { rssi_byte };
|
||
|
||
let presence_valid = (quality_flags & (1 << 0)) != 0;
|
||
// Threshold lowered from 0.5 to 0.15 for low-SNR multi-meter deployments
|
||
// where FW's broadband-variance motion rarely saturates above 0.5.
|
||
let presence = presence_valid && presence_score > 0.15;
|
||
let fall_detected = (quality_flags & (1 << 3)) != 0;
|
||
let motion = motion_score > 0.05;
|
||
let n_persons = if presence { 1 } else { 0 };
|
||
|
||
Some(Esp32VitalsPacket {
|
||
node_id,
|
||
presence,
|
||
fall_detected,
|
||
motion,
|
||
breathing_rate_bpm: respiration_bpm as f64,
|
||
heartrate_bpm: heartbeat_bpm as f64,
|
||
rssi,
|
||
n_persons,
|
||
motion_energy: motion_score,
|
||
presence_score,
|
||
timestamp_ms: (ts_us / 1000) as u32,
|
||
})
|
||
}
|
||
|
||
/// Parse a 32-byte edge vitals packet (magic 0xC511_0002).
|
||
fn parse_esp32_vitals(buf: &[u8]) -> Option<Esp32VitalsPacket> {
|
||
if buf.len() < 32 {
|
||
return None;
|
||
}
|
||
let magic = u32::from_le_bytes([buf[0], buf[1], buf[2], buf[3]]);
|
||
if magic != 0xC511_0002 {
|
||
return None;
|
||
}
|
||
|
||
let node_id = buf[4];
|
||
let flags = buf[5];
|
||
let breathing_raw = u16::from_le_bytes([buf[6], buf[7]]);
|
||
let heartrate_raw = u32::from_le_bytes([buf[8], buf[9], buf[10], buf[11]]);
|
||
let rssi = buf[12] as i8;
|
||
let n_persons = buf[13];
|
||
let motion_energy = f32::from_le_bytes([buf[16], buf[17], buf[18], buf[19]]);
|
||
let presence_score = f32::from_le_bytes([buf[20], buf[21], buf[22], buf[23]]);
|
||
let timestamp_ms = u32::from_le_bytes([buf[24], buf[25], buf[26], buf[27]]);
|
||
|
||
Some(Esp32VitalsPacket {
|
||
node_id,
|
||
presence: (flags & 0x01) != 0,
|
||
fall_detected: (flags & 0x02) != 0,
|
||
motion: (flags & 0x04) != 0,
|
||
breathing_rate_bpm: breathing_raw as f64 / 100.0,
|
||
heartrate_bpm: heartrate_raw as f64 / 10000.0,
|
||
rssi,
|
||
n_persons,
|
||
motion_energy,
|
||
presence_score,
|
||
timestamp_ms,
|
||
})
|
||
}
|
||
|
||
// ── ADR-040: WASM Output Packet (magic 0xC511_0004) ───────────────────────────
|
||
|
||
/// Single WASM event (type + value).
|
||
#[derive(Debug, Clone, Serialize)]
|
||
struct WasmEvent {
|
||
event_type: u8,
|
||
value: f32,
|
||
}
|
||
|
||
/// Decoded WASM output packet from ESP32 Tier 3 runtime.
|
||
#[derive(Debug, Clone, Serialize)]
|
||
struct WasmOutputPacket {
|
||
node_id: u8,
|
||
module_id: u8,
|
||
events: Vec<WasmEvent>,
|
||
}
|
||
|
||
/// Parse a WASM output packet (magic 0xC511_0004).
|
||
fn parse_wasm_output(buf: &[u8]) -> Option<WasmOutputPacket> {
|
||
if buf.len() < 8 {
|
||
return None;
|
||
}
|
||
let magic = u32::from_le_bytes([buf[0], buf[1], buf[2], buf[3]]);
|
||
if magic != 0xC511_0004 {
|
||
return None;
|
||
}
|
||
|
||
let node_id = buf[4];
|
||
let module_id = buf[5];
|
||
let event_count = u16::from_le_bytes([buf[6], buf[7]]) as usize;
|
||
|
||
let mut events = Vec::with_capacity(event_count);
|
||
let mut offset = 8;
|
||
for _ in 0..event_count {
|
||
if offset + 5 > buf.len() {
|
||
break;
|
||
}
|
||
let event_type = buf[offset];
|
||
let value = f32::from_le_bytes([
|
||
buf[offset + 1], buf[offset + 2], buf[offset + 3], buf[offset + 4],
|
||
]);
|
||
events.push(WasmEvent { event_type, value });
|
||
offset += 5;
|
||
}
|
||
|
||
Some(WasmOutputPacket {
|
||
node_id,
|
||
module_id,
|
||
events,
|
||
})
|
||
}
|
||
|
||
// ── FW5.47 CSI_LEAN text packet parser ───────────────────────────────────────
|
||
//
|
||
// FW5.47 (esp32s3_csi_capture) emits compact CSV-style UDP packets:
|
||
// CSI_LEAN,role,src_mac,dst_mac,rssi,noise,channel,ts,seq,n_subc,profile,"[a1 a2 a3 ...]"
|
||
//
|
||
// The bracketed array contains `n_subc` uint8 amplitude bins (already
|
||
// magnitude-summarised on-device). We convert into Esp32Frame with
|
||
// amplitudes filled (phases = 0) so the existing DSP pipeline can consume it.
|
||
fn parse_csi_lean(buf: &[u8]) -> Option<Esp32Frame> {
|
||
// Cheap prefix check before doing UTF-8 decode.
|
||
if buf.len() < 10 || &buf[0..9] != b"CSI_LEAN," {
|
||
return None;
|
||
}
|
||
let text = std::str::from_utf8(buf).ok()?;
|
||
|
||
// Find amplitude array between the first '[' and ']'.
|
||
let lb = text.find('[')?;
|
||
let rb = text[lb..].find(']')?;
|
||
let arr = &text[lb + 1..lb + rb];
|
||
|
||
// Header part is comma-separated, up to the '"[' chunk.
|
||
// Fields (1-indexed):
|
||
// 1: role(int), 2: src_mac, 3: dst_mac, 4: rssi(int), 5: noise(int),
|
||
// 6: channel(int), 7: ts(int), 8: seq(uint), 9: n_subc(uint),
|
||
// 10: profile_name, 11+: array (handled separately).
|
||
let head: Vec<&str> = text[..lb].split(',').collect();
|
||
if head.len() < 10 { return None; }
|
||
|
||
let _role = head[1].trim().parse::<u8>().unwrap_or(1);
|
||
let src_mac = head[2].trim();
|
||
let _dst_mac = head[3];
|
||
let rssi: i8 = head[4].trim().parse().unwrap_or(-60);
|
||
let noise: i8 = head[5].trim().parse().unwrap_or(-95);
|
||
let channel: u16 = head[6].trim().parse().unwrap_or(0);
|
||
let sequence: u32 = head[8].trim().parse().unwrap_or(0);
|
||
let n_subc: u32 = head[9].trim().parse().unwrap_or(64);
|
||
|
||
let mut amplitudes: Vec<f64> = arr
|
||
.split_whitespace()
|
||
.filter_map(|t| t.parse::<u32>().ok())
|
||
.map(|v| v as f64)
|
||
.collect();
|
||
|
||
if amplitudes.is_empty() { return None; }
|
||
// Guard length to what header claims, padding zeros if short.
|
||
if amplitudes.len() < n_subc as usize {
|
||
amplitudes.resize(n_subc as usize, 0.0);
|
||
} else if amplitudes.len() > n_subc as usize {
|
||
amplitudes.truncate(n_subc as usize);
|
||
}
|
||
let phases: Vec<f64> = vec![0.0; amplitudes.len()];
|
||
|
||
// Derive node_id from source MAC last octet (unique per board).
|
||
// Hard-mapped for the known room sensors so labels match physical units.
|
||
let node_id: u8 = match src_mac.to_ascii_lowercase().as_str() {
|
||
"1c:db:d4:49:eb:88" => 1, // room01
|
||
"e8:f6:0a:83:89:44" => 2, // room02
|
||
_ => {
|
||
// Fallback: parse last MAC octet from "xx:xx:xx:xx:xx:NN"
|
||
src_mac.rsplit(':').next()
|
||
.and_then(|h| u8::from_str_radix(h, 16).ok())
|
||
.unwrap_or(1)
|
||
}
|
||
};
|
||
|
||
// Channel → freq_mhz approximation (2.4 GHz band).
|
||
let freq_mhz = if channel >= 1 && channel <= 14 {
|
||
2407u16 + 5 * channel
|
||
} else {
|
||
2412u16
|
||
};
|
||
|
||
Some(Esp32Frame {
|
||
magic: 0xC511_0001,
|
||
node_id,
|
||
n_antennas: 1,
|
||
n_subcarriers: amplitudes.len() as u8,
|
||
freq_mhz,
|
||
sequence,
|
||
rssi: if rssi > 0 { -rssi } else { rssi },
|
||
noise_floor: noise,
|
||
amplitudes,
|
||
phases,
|
||
sensor_timestamp_us: None,
|
||
})
|
||
}
|
||
|
||
// ── ESP32 UDP frame parser ───────────────────────────────────────────────────
|
||
|
||
fn parse_esp32_frame(buf: &[u8]) -> Option<Esp32Frame> {
|
||
if buf.len() < 20 {
|
||
return None;
|
||
}
|
||
|
||
let magic = u32::from_le_bytes([buf[0], buf[1], buf[2], buf[3]]);
|
||
if magic != 0xC511_0001 {
|
||
return None;
|
||
}
|
||
|
||
// Frame layout (must match firmware csi_collector.c):
|
||
// [0..3] magic (u32 LE)
|
||
// [4] node_id (u8)
|
||
// [5] n_antennas (u8)
|
||
// [6..7] n_subcarriers (u16 LE)
|
||
// [8..11] freq_mhz (u32 LE)
|
||
// [12..15] sequence (u32 LE)
|
||
// [16] rssi (i8)
|
||
// [17] noise_floor (i8)
|
||
// [18..19] reserved
|
||
// [20..] I/Q data
|
||
//
|
||
// ADR-100 D3 fix: previous code read every field after `n_antennas`
|
||
// from offsets shifted by 2 bytes (n_subcarriers as u8 instead of u16,
|
||
// sequence at 10..14, rssi at 14, noise_floor at 15). That made the
|
||
// RSSI byte a slice of mid-sequence number — random — and the
|
||
// saturating_neg() workaround hid this by always producing a negative
|
||
// value. Now matches FW byte-for-byte. The csi.rs duplicate of this
|
||
// function had the same bug and is fixed in the same change.
|
||
let node_id = buf[4];
|
||
let n_antennas = buf[5];
|
||
let n_subcarriers = u16::from_le_bytes([buf[6], buf[7]]) as u8;
|
||
let freq_mhz = u16::from_le_bytes([buf[8], buf[9]]);
|
||
let sequence = u32::from_le_bytes([buf[12], buf[13], buf[14], buf[15]]);
|
||
let rssi = buf[16] as i8; // already signed in [-128..127]
|
||
let noise_floor = buf[17] as i8;
|
||
|
||
let iq_start = 20;
|
||
let n_pairs = n_antennas as usize * n_subcarriers as usize;
|
||
let expected_len = iq_start + n_pairs * 2;
|
||
|
||
if buf.len() < expected_len {
|
||
return None;
|
||
}
|
||
|
||
let mut amplitudes = Vec::with_capacity(n_pairs);
|
||
let mut phases = Vec::with_capacity(n_pairs);
|
||
|
||
for k in 0..n_pairs {
|
||
let i_val = buf[iq_start + k * 2] as i8 as f64;
|
||
let q_val = buf[iq_start + k * 2 + 1] as i8 as f64;
|
||
amplitudes.push((i_val * i_val + q_val * q_val).sqrt());
|
||
phases.push(q_val.atan2(i_val));
|
||
}
|
||
|
||
Some(Esp32Frame {
|
||
magic,
|
||
node_id,
|
||
n_antennas,
|
||
n_subcarriers,
|
||
freq_mhz,
|
||
sequence,
|
||
rssi,
|
||
noise_floor,
|
||
amplitudes,
|
||
phases,
|
||
// ADR-106: trailing 4-byte sensor µs timestamp from new FW.
|
||
// Old FW: buf has exactly `iq_start + iq_len` bytes ⇒ Option::None.
|
||
// New FW: 4 more bytes after I/Q ⇒ parse as u32 LE.
|
||
sensor_timestamp_us: if buf.len() >= expected_len + 4 {
|
||
Some(u32::from_le_bytes([
|
||
buf[expected_len], buf[expected_len + 1],
|
||
buf[expected_len + 2], buf[expected_len + 3],
|
||
]))
|
||
} else { None },
|
||
})
|
||
}
|
||
|
||
// ── Signal field generation ──────────────────────────────────────────────────
|
||
|
||
/// Generate a signal field that reflects where motion and signal changes are occurring.
|
||
///
|
||
/// Instead of a fixed-animation circle, this function uses the actual sensing data:
|
||
/// - `subcarrier_variances`: per-subcarrier variance computed from the frame history.
|
||
/// High-variance subcarriers indicate spatial directions where the signal is disrupted.
|
||
/// - `motion_score`: overall motion intensity [0, 1].
|
||
/// - `breathing_rate_hz`: estimated breathing rate in Hz; if > 0, adds a breathing ring.
|
||
/// - `signal_quality`: overall quality metric [0, 1] modulates field brightness.
|
||
///
|
||
/// The field grid is 20×20 cells representing a top-down view of the room.
|
||
/// Hotspots are derived from the subcarrier index (treated as an angular bin) so that
|
||
/// subcarriers with the highest variance produce peaks at the corresponding directions.
|
||
fn generate_signal_field(
|
||
_mean_rssi: f64,
|
||
_motion_score: f64,
|
||
_breathing_rate_hz: f64,
|
||
_signal_quality: f64,
|
||
_subcarrier_variances: &[f64],
|
||
) -> SignalField {
|
||
// ADR-105: this used to paint a 20×20 "room heatmap" by mapping each
|
||
// subcarrier index `k` to an angle `2π·k/N` and dropping a Gaussian
|
||
// hotspot at radius proportional to its variance — visually rich, but
|
||
// **physically meaningless**. A single sensor has no directional
|
||
// information, so the resulting hotspots have no correspondence to
|
||
// where anything actually is in the room. Operator requested
|
||
// boots-on-the-ground honesty: return a zero-filled grid. UI will
|
||
// render blank, which is the truthful state until a real
|
||
// multistatic localizer is wired in.
|
||
let grid = 20usize;
|
||
return SignalField { grid_size: [grid, 1, grid], values: vec![0.0; grid * grid] };
|
||
|
||
}
|
||
|
||
/// ADR-112: physically-grounded signal_field for multi-node deployments.
|
||
///
|
||
/// When `MultistaticFuser` succeeds with ≥ 2 contributing nodes, render a
|
||
/// 20×20 spatial heatmap by overlaying isotropic Gaussian "influence"
|
||
/// kernels at each node's configured position, scaled by the global
|
||
/// post-fusion activity (CV² of fused amplitude × cross-node coherence).
|
||
///
|
||
/// **What this map honestly shows**: regions of overlap between the
|
||
/// physical coverage zones of active sensors, modulated by how much
|
||
/// post-fusion CSI activity those sensors collectively see. Bright cells
|
||
/// = multiple sensors close by AND seeing modulation.
|
||
///
|
||
/// **What this map does NOT claim**: the position of a person. We do
|
||
/// not have phase-coherent ranging on commodity ESP32s (no UWB, no two-
|
||
/// way ranging), so any "location" rendered would be guessing. The map
|
||
/// is a *coverage × activity* visualization, deliberately not a
|
||
/// *target localization*.
|
||
///
|
||
/// On `< 2` active nodes or fusion failure, returns the same zero grid
|
||
/// `generate_signal_field` produces — preserving ADR-105's honesty
|
||
/// contract.
|
||
fn signal_field_from_multistatic(
|
||
fuser: &wifi_densepose_signal::ruvsense::multistatic::MultistaticFuser,
|
||
node_states: &std::collections::HashMap<u8, NodeState>,
|
||
) -> SignalField {
|
||
let grid = 20usize;
|
||
let zero = || SignalField { grid_size: [grid, 1, grid], values: vec![0.0; grid * grid] };
|
||
|
||
let (fused_opt, _) = multistatic_bridge::fuse_or_fallback(fuser, node_states);
|
||
let fused = match fused_opt {
|
||
Some(f) if f.active_nodes >= 2 && !f.node_positions.is_empty() => f,
|
||
_ => return zero(),
|
||
};
|
||
|
||
// Global activity proxy: CV² of fused amplitude × cross-node coherence.
|
||
// Both factors are in [0, 1]; their product gates the field on the
|
||
// simultaneous presence of CSI modulation AND inter-node agreement.
|
||
let amp = &fused.fused_amplitude;
|
||
if amp.is_empty() { return zero(); }
|
||
let mean = amp.iter().map(|&v| v as f64).sum::<f64>() / amp.len() as f64;
|
||
let var: f64 = amp.iter().map(|&v| {
|
||
let d = v as f64 - mean; d * d
|
||
}).sum::<f64>() / amp.len() as f64;
|
||
let cv2 = if mean.abs() > 1e-6 {
|
||
(var / (mean * mean)).clamp(0.0, 1.0)
|
||
} else {
|
||
0.0
|
||
};
|
||
let coherence = (fused.cross_node_coherence as f64).clamp(0.0, 1.0);
|
||
let global_activity = cv2 * coherence;
|
||
if global_activity < 1e-3 {
|
||
return zero();
|
||
}
|
||
|
||
// Render in metric room coords. ROOM_EXTENT_M = half-width of the
|
||
// square room footprint the grid spans; SIGMA_M sets the kernel
|
||
// radius (Pace's ESPectre uses a similar σ ≈ room/4 heuristic).
|
||
// The grid spans [-ROOM_EXTENT_M, +ROOM_EXTENT_M] on both axes.
|
||
const ROOM_EXTENT_M: f64 = 3.0;
|
||
const SIGMA_M: f64 = ROOM_EXTENT_M / 4.0;
|
||
let two_sigma2 = 2.0 * SIGMA_M * SIGMA_M;
|
||
let cell_m = (2.0 * ROOM_EXTENT_M) / grid as f64;
|
||
|
||
let mut values = vec![0.0_f64; grid * grid];
|
||
for gy in 0..grid {
|
||
let py = -ROOM_EXTENT_M + (gy as f64 + 0.5) * cell_m;
|
||
for gx in 0..grid {
|
||
let px = -ROOM_EXTENT_M + (gx as f64 + 0.5) * cell_m;
|
||
let mut sum = 0.0_f64;
|
||
for n in &fused.node_positions {
|
||
// Project the 3D node position to the (x, z) floor plane
|
||
// (y = height, irrelevant for a 2D footprint view).
|
||
let nx = n[0] as f64;
|
||
let nz = n[2] as f64;
|
||
let dx = px - nx;
|
||
let dy = py - nz;
|
||
let d2 = dx * dx + dy * dy;
|
||
sum += global_activity * (-d2 / two_sigma2).exp();
|
||
}
|
||
values[gy * grid + gx] = sum.clamp(0.0, 1.0);
|
||
}
|
||
}
|
||
SignalField { grid_size: [grid, 1, grid], values }
|
||
}
|
||
|
||
// ── Feature extraction from ESP32 frame ──────────────────────────────────────
|
||
|
||
/// Estimate breathing rate in Hz from the amplitude time series stored in `frame_history`.
|
||
///
|
||
/// Approach:
|
||
/// 1. Build a scalar time series by computing the mean amplitude of each historical frame.
|
||
/// 2. Run a peak-detection pass: count rising-edge zero-crossings of the de-meaned signal.
|
||
/// 3. Convert the crossing rate to Hz, clipped to the physiological range 0.1–0.5 Hz
|
||
/// (12–30 breaths/min).
|
||
///
|
||
/// For accuracy the function additionally applies a simple 3-tap Goertzel-style power
|
||
/// estimate at evenly-spaced candidate frequencies in the breathing band and returns
|
||
/// the candidate with the highest energy.
|
||
fn estimate_breathing_rate_hz(frame_history: &VecDeque<Vec<f64>>, sample_rate_hz: f64) -> f64 {
|
||
let n = frame_history.len();
|
||
if n < 6 {
|
||
return 0.0;
|
||
}
|
||
|
||
// Build scalar time series: mean amplitude per frame.
|
||
let series: Vec<f64> = frame_history.iter()
|
||
.map(|amps| {
|
||
if amps.is_empty() { 0.0 } else { amps.iter().sum::<f64>() / amps.len() as f64 }
|
||
})
|
||
.collect();
|
||
|
||
let mean_s = series.iter().sum::<f64>() / n as f64;
|
||
// De-mean.
|
||
let detrended: Vec<f64> = series.iter().map(|x| x - mean_s).collect();
|
||
|
||
// Goertzel power at candidate frequencies in the breathing band [0.1, 0.5] Hz.
|
||
// We evaluate 9 candidate frequencies uniformly spaced in that band.
|
||
let n_candidates = 9usize;
|
||
let f_low = 0.1f64;
|
||
let f_high = 0.5f64;
|
||
let mut best_freq = 0.0f64;
|
||
let mut best_power = 0.0f64;
|
||
|
||
for i in 0..n_candidates {
|
||
let freq = f_low + (f_high - f_low) * i as f64 / (n_candidates - 1).max(1) as f64;
|
||
let omega = 2.0 * std::f64::consts::PI * freq / sample_rate_hz;
|
||
let coeff = 2.0 * omega.cos();
|
||
let mut s_prev2 = 0.0f64;
|
||
let mut s_prev1 = 0.0f64;
|
||
for &x in &detrended {
|
||
let s = x + coeff * s_prev1 - s_prev2;
|
||
s_prev2 = s_prev1;
|
||
s_prev1 = s;
|
||
}
|
||
// Goertzel magnitude squared.
|
||
let power = s_prev2 * s_prev2 + s_prev1 * s_prev1 - coeff * s_prev1 * s_prev2;
|
||
if power > best_power {
|
||
best_power = power;
|
||
best_freq = freq;
|
||
}
|
||
}
|
||
|
||
// Only report a breathing rate if the Goertzel energy is meaningfully above noise.
|
||
// Threshold: power must exceed 10× the average power across all candidates.
|
||
let avg_power = {
|
||
let mut total = 0.0f64;
|
||
for i in 0..n_candidates {
|
||
let freq = f_low + (f_high - f_low) * i as f64 / (n_candidates - 1).max(1) as f64;
|
||
let omega = 2.0 * std::f64::consts::PI * freq / sample_rate_hz;
|
||
let coeff = 2.0 * omega.cos();
|
||
let mut s_prev2 = 0.0f64;
|
||
let mut s_prev1 = 0.0f64;
|
||
for &x in &detrended {
|
||
let s = x + coeff * s_prev1 - s_prev2;
|
||
s_prev2 = s_prev1;
|
||
s_prev1 = s;
|
||
}
|
||
total += s_prev2 * s_prev2 + s_prev1 * s_prev1 - coeff * s_prev1 * s_prev2;
|
||
}
|
||
total / n_candidates as f64
|
||
};
|
||
|
||
if best_power > avg_power * 3.0 {
|
||
best_freq.clamp(f_low, f_high)
|
||
} else {
|
||
0.0
|
||
}
|
||
}
|
||
|
||
/// Compute per-subcarrier variance across the sliding window of `frame_history`.
|
||
///
|
||
/// For each subcarrier index `k`, returns `Var[A_k]` over all stored frames.
|
||
/// This captures spatial signal variation; subcarriers whose amplitude fluctuates
|
||
/// heavily across time correspond to directions with motion.
|
||
/// Compute per-subcarrier importance weights using a simple sensitivity split.
|
||
///
|
||
/// Subcarriers whose sensitivity (amplitude magnitude) is above the median are
|
||
/// considered "sensitive" and receive weight `1.0 + (sens / max_sens)` (range 1.0–2.0).
|
||
/// The rest receive a baseline weight of 0.5. This mirrors the RuVector mincut
|
||
/// partition logic without requiring the graph dependency.
|
||
fn compute_subcarrier_importance_weights(sensitivity: &[f64]) -> Vec<f64> {
|
||
let n = sensitivity.len();
|
||
if n == 0 {
|
||
return vec![];
|
||
}
|
||
let max_sens = sensitivity.iter().cloned().fold(f64::NEG_INFINITY, f64::max).max(1e-9);
|
||
|
||
// Compute median via a sorted copy.
|
||
let mut sorted = sensitivity.to_vec();
|
||
sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
|
||
let median = if n % 2 == 0 {
|
||
(sorted[n / 2 - 1] + sorted[n / 2]) / 2.0
|
||
} else {
|
||
sorted[n / 2]
|
||
};
|
||
|
||
sensitivity
|
||
.iter()
|
||
.map(|&s| {
|
||
if s >= median {
|
||
1.0 + (s / max_sens).min(1.0)
|
||
} else {
|
||
0.5
|
||
}
|
||
})
|
||
.collect()
|
||
}
|
||
|
||
fn compute_subcarrier_variances(frame_history: &VecDeque<Vec<f64>>, n_sub: usize) -> Vec<f64> {
|
||
if frame_history.is_empty() || n_sub == 0 {
|
||
return vec![0.0; n_sub];
|
||
}
|
||
|
||
let n_frames = frame_history.len() as f64;
|
||
let mut means = vec![0.0f64; n_sub];
|
||
let mut sq_means = vec![0.0f64; n_sub];
|
||
|
||
for frame in frame_history.iter() {
|
||
for k in 0..n_sub {
|
||
let a = if k < frame.len() { frame[k] } else { 0.0 };
|
||
means[k] += a;
|
||
sq_means[k] += a * a;
|
||
}
|
||
}
|
||
|
||
(0..n_sub)
|
||
.map(|k| {
|
||
let mean = means[k] / n_frames;
|
||
let sq_mean = sq_means[k] / n_frames;
|
||
(sq_mean - mean * mean).max(0.0)
|
||
})
|
||
.collect()
|
||
}
|
||
|
||
/// Extract features from the current ESP32 frame, enhanced with temporal context from
|
||
/// `frame_history`.
|
||
///
|
||
/// Improvements over the previous single-frame approach:
|
||
///
|
||
/// - **Variance**: computed as the mean of per-subcarrier temporal variance across the
|
||
/// sliding window, not just the intra-frame spatial variance.
|
||
/// - **Motion detection**: uses frame-to-frame temporal difference (mean L2 change
|
||
/// between the current frame and the previous frame) normalised by signal amplitude,
|
||
/// so that actual changes are detected rather than just a threshold on the current frame.
|
||
/// - **Breathing rate**: estimated via Goertzel filter bank on the 0.1–0.5 Hz band of
|
||
/// the amplitude time series.
|
||
/// - **Signal quality**: based on SNR estimate (RSSI – noise floor) and subcarrier
|
||
/// variance stability.
|
||
/// Returns (features, raw_classification, breathing_rate_hz, sub_variances, raw_motion_score).
|
||
fn extract_features_from_frame(
|
||
frame: &Esp32Frame,
|
||
frame_history: &VecDeque<Vec<f64>>,
|
||
sample_rate_hz: f64,
|
||
) -> (FeatureInfo, ClassificationInfo, f64, Vec<f64>, f64) {
|
||
let n_sub = frame.amplitudes.len().max(1);
|
||
let n = n_sub as f64;
|
||
let mean_rssi = frame.rssi as f64;
|
||
|
||
// ── RuVector Phase 1: subcarrier importance weighting ──
|
||
// Compute per-subcarrier sensitivity from amplitude magnitude, then weight
|
||
// sensitive subcarriers higher (>1.0) and insensitive ones lower (0.5).
|
||
// This emphasises body-motion-correlated subcarriers in all downstream metrics.
|
||
let sub_sensitivity: Vec<f64> = frame.amplitudes.iter().map(|a| a.abs()).collect();
|
||
let importance_weights = compute_subcarrier_importance_weights(&sub_sensitivity);
|
||
|
||
let weight_sum: f64 = importance_weights.iter().sum::<f64>();
|
||
let mean_amp: f64 = if weight_sum > 0.0 {
|
||
frame.amplitudes.iter().zip(importance_weights.iter())
|
||
.map(|(a, w)| a * w)
|
||
.sum::<f64>() / weight_sum
|
||
} else {
|
||
frame.amplitudes.iter().sum::<f64>() / n
|
||
};
|
||
|
||
// ── Intra-frame subcarrier variance (weighted by importance) ──
|
||
let intra_variance: f64 = if weight_sum > 0.0 {
|
||
frame.amplitudes.iter().zip(importance_weights.iter())
|
||
.map(|(a, w)| w * (a - mean_amp).powi(2))
|
||
.sum::<f64>() / weight_sum
|
||
} else {
|
||
frame.amplitudes.iter()
|
||
.map(|a| (a - mean_amp).powi(2))
|
||
.sum::<f64>() / n
|
||
};
|
||
|
||
// ── Temporal (sliding-window) per-subcarrier variance ──
|
||
let sub_variances = compute_subcarrier_variances(frame_history, n_sub);
|
||
let temporal_variance: f64 = if sub_variances.is_empty() {
|
||
intra_variance
|
||
} else {
|
||
sub_variances.iter().sum::<f64>() / sub_variances.len() as f64
|
||
};
|
||
|
||
// Use the larger of intra-frame and temporal variance as the reported variance.
|
||
let variance = intra_variance.max(temporal_variance);
|
||
|
||
// ── Spectral power ──
|
||
let spectral_power: f64 = frame.amplitudes.iter().map(|a| a * a).sum::<f64>() / n;
|
||
|
||
// ── Motion band power (upper half of subcarriers, high spatial frequency) ──
|
||
let half = frame.amplitudes.len() / 2;
|
||
let motion_band_power = if half > 0 {
|
||
frame.amplitudes[half..].iter()
|
||
.map(|a| (a - mean_amp).powi(2))
|
||
.sum::<f64>() / (frame.amplitudes.len() - half) as f64
|
||
} else {
|
||
0.0
|
||
};
|
||
|
||
// ── Breathing band power (lower half of subcarriers, low spatial frequency) ──
|
||
let breathing_band_power = if half > 0 {
|
||
frame.amplitudes[..half].iter()
|
||
.map(|a| (a - mean_amp).powi(2))
|
||
.sum::<f64>() / half as f64
|
||
} else {
|
||
0.0
|
||
};
|
||
|
||
// ── Dominant frequency via peak subcarrier index ──
|
||
let peak_idx = frame.amplitudes.iter()
|
||
.enumerate()
|
||
.max_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
|
||
.map(|(i, _)| i)
|
||
.unwrap_or(0);
|
||
let dominant_freq_hz = peak_idx as f64 * 0.05;
|
||
|
||
// ── Change point detection (threshold-crossing count in current frame) ──
|
||
let threshold = mean_amp * 1.2;
|
||
let change_points = frame.amplitudes.windows(2)
|
||
.filter(|w| (w[0] < threshold) != (w[1] < threshold))
|
||
.count();
|
||
|
||
// ── Motion score: sliding-window temporal difference ──
|
||
// Compare current frame against the most recent historical frame.
|
||
// The difference is normalised by the mean amplitude to be scale-invariant.
|
||
let temporal_motion_score = if let Some(prev_frame) = frame_history.back() {
|
||
let n_cmp = n_sub.min(prev_frame.len());
|
||
if n_cmp > 0 {
|
||
let diff_energy: f64 = (0..n_cmp)
|
||
.map(|k| (frame.amplitudes[k] - prev_frame[k]).powi(2))
|
||
.sum::<f64>() / n_cmp as f64;
|
||
// Normalise by mean squared amplitude to get a dimensionless ratio.
|
||
let ref_energy = mean_amp * mean_amp + 1e-9;
|
||
(diff_energy / ref_energy).sqrt().clamp(0.0, 1.0)
|
||
} else {
|
||
0.0
|
||
}
|
||
} else {
|
||
// No history yet — fall back to intra-frame variance-based estimate.
|
||
(intra_variance / (mean_amp * mean_amp + 1e-9)).sqrt().clamp(0.0, 1.0)
|
||
};
|
||
|
||
// Blend temporal motion with variance-based motion for robustness.
|
||
// Also factor in motion_band_power and change_points for ESP32 real-world sensitivity.
|
||
let variance_motion = (temporal_variance / 10.0).clamp(0.0, 1.0);
|
||
let mbp_motion = (motion_band_power / 25.0).clamp(0.0, 1.0);
|
||
let cp_motion = (change_points as f64 / 15.0).clamp(0.0, 1.0);
|
||
let motion_score = (temporal_motion_score * 0.4 + variance_motion * 0.2 + mbp_motion * 0.25 + cp_motion * 0.15).clamp(0.0, 1.0);
|
||
|
||
// ── Signal quality metric ──
|
||
// Based on estimated SNR (RSSI relative to noise floor) and subcarrier consistency.
|
||
let snr_db = (frame.rssi as f64 - frame.noise_floor as f64).max(0.0);
|
||
let snr_quality = (snr_db / 40.0).clamp(0.0, 1.0); // 40 dB → quality = 1.0
|
||
// Penalise quality when temporal variance is very high (unstable signal).
|
||
let stability = (1.0 - (temporal_variance / (mean_amp * mean_amp + 1e-9)).clamp(0.0, 1.0)).max(0.0);
|
||
let signal_quality = (snr_quality * 0.6 + stability * 0.4).clamp(0.0, 1.0);
|
||
|
||
// ── Breathing rate estimation ──
|
||
let breathing_rate_hz = estimate_breathing_rate_hz(frame_history, sample_rate_hz);
|
||
|
||
let features = FeatureInfo {
|
||
mean_rssi,
|
||
variance,
|
||
motion_band_power,
|
||
breathing_band_power,
|
||
dominant_freq_hz,
|
||
change_points,
|
||
spectral_power,
|
||
};
|
||
|
||
// Return raw motion_score and signal_quality — classification is done by
|
||
// `smooth_and_classify()` which has access to EMA state and hysteresis.
|
||
let raw_classification = ClassificationInfo {
|
||
motion_level: raw_classify(motion_score),
|
||
presence: motion_score > 0.04,
|
||
confidence: (0.4 + signal_quality * 0.3 + motion_score * 0.3).clamp(0.0, 1.0),
|
||
};
|
||
|
||
(features, raw_classification, breathing_rate_hz, sub_variances, motion_score)
|
||
}
|
||
|
||
/// Simple threshold classification (no smoothing) — used as the "raw" input.
|
||
fn raw_classify(score: f64) -> String {
|
||
if score > 0.25 { "active".into() }
|
||
else if score > 0.12 { "present_moving".into() }
|
||
else if score > 0.04 { "present_still".into() }
|
||
else { "absent".into() }
|
||
}
|
||
|
||
/// Debounce frames required before state transition (at ~10 FPS = ~0.4s).
|
||
const DEBOUNCE_FRAMES: u32 = 4;
|
||
/// EMA alpha for motion smoothing (~1s time constant at 10 FPS).
|
||
const MOTION_EMA_ALPHA: f64 = 0.15;
|
||
/// EMA alpha for slow-adapting baseline (~30s time constant at 10 FPS).
|
||
const BASELINE_EMA_ALPHA: f64 = 0.003;
|
||
/// Number of warm-up frames before baseline subtraction kicks in.
|
||
const BASELINE_WARMUP: u64 = 50;
|
||
|
||
/// Apply EMA smoothing, adaptive baseline subtraction, and hysteresis debounce
|
||
/// to the raw classification. Mutates the smoothing state in `AppStateInner`.
|
||
fn smooth_and_classify(state: &mut AppStateInner, raw: &mut ClassificationInfo, raw_motion: f64) {
|
||
// 1. Adaptive baseline: slowly track the "quiet room" floor.
|
||
// Only update baseline when raw score is below the current smoothed level
|
||
// (i.e. during calm periods) so walking doesn't inflate the baseline.
|
||
state.baseline_frames += 1;
|
||
if state.baseline_frames < BASELINE_WARMUP {
|
||
// During warm-up, aggressively learn the baseline.
|
||
state.baseline_motion = state.baseline_motion * 0.9 + raw_motion * 0.1;
|
||
} else if raw_motion < state.smoothed_motion + 0.05 {
|
||
state.baseline_motion = state.baseline_motion * (1.0 - BASELINE_EMA_ALPHA)
|
||
+ raw_motion * BASELINE_EMA_ALPHA;
|
||
}
|
||
|
||
// 2. Subtract baseline and clamp.
|
||
let adjusted = (raw_motion - state.baseline_motion * 0.7).max(0.0);
|
||
|
||
// 3. EMA smooth the adjusted score.
|
||
state.smoothed_motion = state.smoothed_motion * (1.0 - MOTION_EMA_ALPHA)
|
||
+ adjusted * MOTION_EMA_ALPHA;
|
||
let sm = state.smoothed_motion;
|
||
|
||
// 4. Classify from smoothed score.
|
||
let candidate = raw_classify(sm);
|
||
|
||
// 5. Hysteresis debounce: require N consecutive frames agreeing on a new state.
|
||
if candidate == state.current_motion_level {
|
||
// Already in this state — reset debounce.
|
||
state.debounce_counter = 0;
|
||
state.debounce_candidate = candidate;
|
||
} else if candidate == state.debounce_candidate {
|
||
state.debounce_counter += 1;
|
||
if state.debounce_counter >= DEBOUNCE_FRAMES {
|
||
// Transition accepted.
|
||
state.current_motion_level = candidate;
|
||
state.debounce_counter = 0;
|
||
}
|
||
} else {
|
||
// New candidate — restart counter.
|
||
state.debounce_candidate = candidate;
|
||
state.debounce_counter = 1;
|
||
}
|
||
|
||
// 6. Write the smoothed result back into the classification.
|
||
raw.motion_level = state.current_motion_level.clone();
|
||
raw.presence = sm > 0.03;
|
||
raw.confidence = (0.4 + sm * 0.6).clamp(0.0, 1.0);
|
||
}
|
||
|
||
/// Per-node variant of `smooth_and_classify` that operates on a `NodeState`
|
||
/// instead of `AppStateInner` (issue #249).
|
||
fn smooth_and_classify_node(ns: &mut NodeState, raw: &mut ClassificationInfo, raw_motion: f64) {
|
||
ns.baseline_frames += 1;
|
||
if ns.baseline_frames < BASELINE_WARMUP {
|
||
ns.baseline_motion = ns.baseline_motion * 0.9 + raw_motion * 0.1;
|
||
} else if raw_motion < ns.smoothed_motion + 0.05 {
|
||
ns.baseline_motion = ns.baseline_motion * (1.0 - BASELINE_EMA_ALPHA)
|
||
+ raw_motion * BASELINE_EMA_ALPHA;
|
||
}
|
||
|
||
let adjusted = (raw_motion - ns.baseline_motion * 0.7).max(0.0);
|
||
|
||
ns.smoothed_motion = ns.smoothed_motion * (1.0 - MOTION_EMA_ALPHA)
|
||
+ adjusted * MOTION_EMA_ALPHA;
|
||
let sm = ns.smoothed_motion;
|
||
|
||
let candidate = raw_classify(sm);
|
||
|
||
if candidate == ns.current_motion_level {
|
||
ns.debounce_counter = 0;
|
||
ns.debounce_candidate = candidate;
|
||
} else if candidate == ns.debounce_candidate {
|
||
ns.debounce_counter += 1;
|
||
if ns.debounce_counter >= DEBOUNCE_FRAMES {
|
||
ns.current_motion_level = candidate;
|
||
ns.debounce_counter = 0;
|
||
}
|
||
} else {
|
||
ns.debounce_candidate = candidate;
|
||
ns.debounce_counter = 1;
|
||
}
|
||
|
||
raw.motion_level = ns.current_motion_level.clone();
|
||
raw.presence = sm > 0.03;
|
||
raw.confidence = (0.4 + sm * 0.6).clamp(0.0, 1.0);
|
||
}
|
||
|
||
/// ADR-118: collect the latest amplitude vector per node from `AMP_HIST`.
|
||
/// The adaptive classifier's new 22-feature pipeline reads 3 features per
|
||
/// node × 6 nodes; calling code at the override sites no longer has access
|
||
/// to a single global "amps" vector — it needs the per-node breakdown.
|
||
fn current_per_node_amps() -> Vec<(u8, Vec<f64>)> {
|
||
let map = amp_hist_init().lock().unwrap();
|
||
map.iter()
|
||
.filter_map(|(nid, st)| {
|
||
st.nbvi_history.back().cloned().map(|amps| (*nid, amps))
|
||
})
|
||
.collect()
|
||
}
|
||
|
||
/// If an adaptive model is loaded, override the classification with the
|
||
/// model's prediction. ADR-120: prefers temporal-window classifier when
|
||
/// the rolling feature buffer is full (20 frames). Falls through to
|
||
/// frame-level (ADR-119 MLP) at cold start.
|
||
///
|
||
/// Read-only over `state` — the per-tick push into `feature_window` happens
|
||
/// at the tick site where `&mut AppStateInner` is already held (see the
|
||
/// broadcast tick task in `run_*_pipeline`).
|
||
fn adaptive_override(state: &AppStateInner, features: &FeatureInfo, classification: &mut ClassificationInfo) {
|
||
if let Some(ref model) = state.adaptive_model {
|
||
let per_node_owned = current_per_node_amps();
|
||
let per_node_refs: Vec<(u8, &[f64])> = per_node_owned.iter()
|
||
.map(|(n, a)| (*n, a.as_slice())).collect();
|
||
let feat_arr = adaptive_classifier::features_from_runtime(
|
||
&serde_json::json!({
|
||
"variance": features.variance,
|
||
"motion_band_power": features.motion_band_power,
|
||
"breathing_band_power": features.breathing_band_power,
|
||
"spectral_power": features.spectral_power,
|
||
"dominant_freq_hz": features.dominant_freq_hz,
|
||
"change_points": features.change_points,
|
||
"mean_rssi": features.mean_rssi,
|
||
}),
|
||
&per_node_refs,
|
||
);
|
||
|
||
// ADR-120: if rolling window has at least the current frame + 19 prior,
|
||
// use the temporal classifier. Otherwise fall back to frame-level.
|
||
let (label, conf) = if state.feature_window.len() + 1 >= adaptive_classifier::WINDOW_FRAMES {
|
||
// Flatten the last (WINDOW_FRAMES - 1) historic vectors + current
|
||
// frame into a single 440-d row-major vector, oldest first.
|
||
let wf = adaptive_classifier::WINDOW_FRAMES;
|
||
let nf = adaptive_classifier::N_FEATURES_PUB;
|
||
let mut flat = vec![0.0f64; wf * nf];
|
||
// History fills the first (WINDOW_FRAMES - 1) frames.
|
||
let hist_take = wf - 1;
|
||
let skip = state.feature_window.len().saturating_sub(hist_take);
|
||
for (frame_i, fv) in state.feature_window.iter().skip(skip).enumerate() {
|
||
let base = frame_i * nf;
|
||
for i in 0..nf { flat[base + i] = fv[i]; }
|
||
}
|
||
// Last slot = current frame.
|
||
let last_base = (wf - 1) * nf;
|
||
for i in 0..nf { flat[last_base + i] = feat_arr[i]; }
|
||
model.classify_window(&flat)
|
||
} else {
|
||
model.classify(&feat_arr)
|
||
};
|
||
|
||
// ADR-120 follow-up #2: emit raw model label here. Smoothing is
|
||
// applied centrally at end-of-tick via finalize_motion_label so
|
||
// it covers BOTH the adaptive path AND the rule-based override
|
||
// paths (amp_presence_override / amp_classify_from_latest) which
|
||
// previously wrote raw values directly to motion_level.
|
||
classification.motion_level = label.to_string();
|
||
classification.presence = label != "absent";
|
||
// Blend model confidence with existing smoothed confidence.
|
||
classification.confidence = (conf * 0.7 + classification.confidence * 0.3).clamp(0.0, 1.0);
|
||
}
|
||
}
|
||
|
||
/// ADR-120 follow-up: two-layer smoothing on the adaptive classifier
|
||
/// output to stop UI flicker.
|
||
///
|
||
/// Layer 1 — majority-vote over the last `ADAPTIVE_SMOOTH_WIN` ticks
|
||
/// (3 sec @ 10 Hz). Brief glitches lose to sustained signal.
|
||
///
|
||
/// Layer 2 — candidate confirmation: even when the layer-1 mode flips,
|
||
/// the committed display label only updates after the new mode has
|
||
/// persisted for `ADAPTIVE_CONFIRM_TICKS` consecutive ticks. Prevents
|
||
/// rapid bouncing between two near-tied classes.
|
||
///
|
||
/// Combined effective dwell time: ≥3 sec before any visible label change.
|
||
/// Live UX target: user can read the badge without it changing
|
||
/// mid-read, while a genuine activity switch still propagates within
|
||
/// ~3-4 seconds.
|
||
const ADAPTIVE_SMOOTH_WIN: usize = 30;
|
||
const ADAPTIVE_CONFIRM_TICKS: u32 = 5;
|
||
|
||
static ADAPTIVE_LABEL_HISTORY: OnceLock<Mutex<VecDeque<String>>> = OnceLock::new();
|
||
/// (committed_label, candidate_label, candidate_consecutive_count)
|
||
static ADAPTIVE_COMMITTED: OnceLock<Mutex<(String, String, u32)>> = OnceLock::new();
|
||
|
||
fn adaptive_label_history_init() -> &'static Mutex<VecDeque<String>> {
|
||
ADAPTIVE_LABEL_HISTORY.get_or_init(|| Mutex::new(VecDeque::with_capacity(ADAPTIVE_SMOOTH_WIN)))
|
||
}
|
||
|
||
fn adaptive_committed_init() -> &'static Mutex<(String, String, u32)> {
|
||
ADAPTIVE_COMMITTED.get_or_init(|| Mutex::new((String::new(), String::new(), 0)))
|
||
}
|
||
|
||
/// ADR-120 follow-up #2: smooth WHATEVER label the cascade of overrides
|
||
/// produced, regardless of source (adaptive model OR amp_presence_override
|
||
/// OR amp_classify_from_latest). The earlier adaptive_label_smooth ONLY
|
||
/// covered the adaptive output — anything else (the 4 baseline classes)
|
||
/// passed through raw, so the live label kept flipping on every tick.
|
||
/// This is the final chokepoint called from each tick handler after all
|
||
/// overrides have run.
|
||
pub fn finalize_motion_label(classification: &mut ClassificationInfo) {
|
||
let smoothed = adaptive_label_smooth(&classification.motion_level);
|
||
classification.presence = smoothed != "absent";
|
||
classification.motion_level = smoothed;
|
||
}
|
||
|
||
/// Push `raw_label` into Layer 1 (rolling history) and compute its mode.
|
||
/// Then run Layer 2 (candidate confirmation): a label different from the
|
||
/// committed one must persist for ADAPTIVE_CONFIRM_TICKS consecutive
|
||
/// mode-results before becoming the new committed.
|
||
fn adaptive_label_smooth(raw_label: &str) -> String {
|
||
// Layer 1 — majority vote.
|
||
let mode = {
|
||
let mut buf = adaptive_label_history_init().lock().unwrap();
|
||
buf.push_back(raw_label.to_string());
|
||
while buf.len() > ADAPTIVE_SMOOTH_WIN { buf.pop_front(); }
|
||
let mut counts: std::collections::HashMap<&str, usize> = std::collections::HashMap::new();
|
||
for v in buf.iter() {
|
||
*counts.entry(v.as_str()).or_insert(0) += 1;
|
||
}
|
||
let mut best = (raw_label.to_string(), 0usize);
|
||
for (k, v) in &counts {
|
||
if *v > best.1 {
|
||
best = ((*k).to_string(), *v);
|
||
}
|
||
}
|
||
best.0
|
||
};
|
||
|
||
// Layer 2 — candidate confirmation.
|
||
let mut st = adaptive_committed_init().lock().unwrap();
|
||
if st.0.is_empty() {
|
||
// Cold start: commit immediately on first non-empty mode.
|
||
st.0 = mode.clone();
|
||
st.1 = mode.clone();
|
||
st.2 = 0;
|
||
return mode;
|
||
}
|
||
if mode == st.0 {
|
||
// Mode agrees with the committed label — reset candidate.
|
||
st.1 = mode;
|
||
st.2 = 0;
|
||
} else if mode == st.1 {
|
||
// Same candidate as before — increment streak.
|
||
st.2 += 1;
|
||
if st.2 >= ADAPTIVE_CONFIRM_TICKS {
|
||
// Confirmed; promote candidate.
|
||
st.0 = st.1.clone();
|
||
st.2 = 0;
|
||
}
|
||
} else {
|
||
// New candidate.
|
||
st.1 = mode;
|
||
st.2 = 1;
|
||
}
|
||
st.0.clone()
|
||
}
|
||
|
||
/// ADR-120: classes that ONLY the adaptive W-MLP model can produce.
|
||
/// The rule-based amp_presence_override / amp_classify_from_latest paths
|
||
/// know only {absent, present_still, present_moving, active}; if the
|
||
/// adaptive model has just emitted `waving` or `transition`, we must NOT
|
||
/// overwrite it with the rule-based output. Hybrid priority: rule-based
|
||
/// wins for the 4 baseline classes (it's battle-tested at F1 > 96%);
|
||
/// adaptive wins exclusively when emitting a class outside that set.
|
||
const ADAPTIVE_EXCLUSIVE_CLASSES: &[&str] = &["waving", "transition"];
|
||
|
||
fn adaptive_owns_class(label: &str) -> bool {
|
||
ADAPTIVE_EXCLUSIVE_CLASSES.iter().any(|&c| c == label)
|
||
}
|
||
|
||
/// ADR-120: push the current frame's feature vector into the rolling
|
||
/// window buffer, evicting the oldest entry when at capacity. Called
|
||
/// once per tick from the broadcast tick task where `&mut AppStateInner`
|
||
/// is already held.
|
||
fn push_feature_window(state: &mut AppStateInner, features: &FeatureInfo) {
|
||
let per_node_owned = current_per_node_amps();
|
||
let per_node_refs: Vec<(u8, &[f64])> = per_node_owned.iter()
|
||
.map(|(n, a)| (*n, a.as_slice())).collect();
|
||
let feat_arr = adaptive_classifier::features_from_runtime(
|
||
&serde_json::json!({
|
||
"variance": features.variance,
|
||
"motion_band_power": features.motion_band_power,
|
||
"breathing_band_power": features.breathing_band_power,
|
||
"spectral_power": features.spectral_power,
|
||
"dominant_freq_hz": features.dominant_freq_hz,
|
||
"change_points": features.change_points,
|
||
"mean_rssi": features.mean_rssi,
|
||
}),
|
||
&per_node_refs,
|
||
);
|
||
state.feature_window.push_back(feat_arr);
|
||
while state.feature_window.len() > adaptive_classifier::WINDOW_FRAMES {
|
||
state.feature_window.pop_front();
|
||
}
|
||
}
|
||
|
||
/// Size of the median filter window for vital signs outlier rejection.
|
||
const VITAL_MEDIAN_WINDOW: usize = 21;
|
||
/// EMA alpha for vital signs (~5s time constant at 10 FPS).
|
||
const VITAL_EMA_ALPHA: f64 = 0.02;
|
||
/// Maximum BPM jump per frame before a value is rejected as an outlier.
|
||
const HR_MAX_JUMP: f64 = 8.0;
|
||
const BR_MAX_JUMP: f64 = 2.0;
|
||
/// Minimum change from current smoothed value before EMA updates (dead-band).
|
||
/// Prevents micro-drift from creeping in.
|
||
const HR_DEAD_BAND: f64 = 2.0;
|
||
const BR_DEAD_BAND: f64 = 0.5;
|
||
|
||
/// Smooth vital signs using median-filter outlier rejection + EMA.
|
||
/// Mutates `state.smoothed_hr`, `state.smoothed_br`, etc.
|
||
/// Returns the smoothed VitalSigns to broadcast.
|
||
fn smooth_vitals(state: &mut AppStateInner, raw: &VitalSigns) -> VitalSigns {
|
||
let raw_hr = raw.heart_rate_bpm.unwrap_or(0.0);
|
||
let raw_br = raw.breathing_rate_bpm.unwrap_or(0.0);
|
||
|
||
// -- Outlier rejection: skip values that jump too far from current EMA --
|
||
let hr_ok = state.smoothed_hr < 1.0 || (raw_hr - state.smoothed_hr).abs() < HR_MAX_JUMP;
|
||
let br_ok = state.smoothed_br < 1.0 || (raw_br - state.smoothed_br).abs() < BR_MAX_JUMP;
|
||
|
||
// Push into buffer (only non-outlier values)
|
||
if hr_ok && raw_hr > 0.0 {
|
||
state.hr_buffer.push_back(raw_hr);
|
||
if state.hr_buffer.len() > VITAL_MEDIAN_WINDOW { state.hr_buffer.pop_front(); }
|
||
}
|
||
if br_ok && raw_br > 0.0 {
|
||
state.br_buffer.push_back(raw_br);
|
||
if state.br_buffer.len() > VITAL_MEDIAN_WINDOW { state.br_buffer.pop_front(); }
|
||
}
|
||
|
||
// Compute trimmed mean: drop top/bottom 25% then average the middle 50%.
|
||
// This is more stable than pure median and less noisy than raw mean.
|
||
let trimmed_hr = trimmed_mean(&state.hr_buffer);
|
||
let trimmed_br = trimmed_mean(&state.br_buffer);
|
||
|
||
// EMA smooth with dead-band: only update if the trimmed mean differs
|
||
// from the current smoothed value by more than the dead-band.
|
||
// This prevents the display from constantly creeping by tiny amounts.
|
||
if trimmed_hr > 0.0 {
|
||
if state.smoothed_hr < 1.0 {
|
||
state.smoothed_hr = trimmed_hr;
|
||
} else if (trimmed_hr - state.smoothed_hr).abs() > HR_DEAD_BAND {
|
||
state.smoothed_hr = state.smoothed_hr * (1.0 - VITAL_EMA_ALPHA)
|
||
+ trimmed_hr * VITAL_EMA_ALPHA;
|
||
}
|
||
// else: within dead-band, hold current value
|
||
}
|
||
if trimmed_br > 0.0 {
|
||
if state.smoothed_br < 1.0 {
|
||
state.smoothed_br = trimmed_br;
|
||
} else if (trimmed_br - state.smoothed_br).abs() > BR_DEAD_BAND {
|
||
state.smoothed_br = state.smoothed_br * (1.0 - VITAL_EMA_ALPHA)
|
||
+ trimmed_br * VITAL_EMA_ALPHA;
|
||
}
|
||
}
|
||
|
||
// Smooth confidence
|
||
state.smoothed_hr_conf = state.smoothed_hr_conf * 0.92 + raw.heartbeat_confidence * 0.08;
|
||
state.smoothed_br_conf = state.smoothed_br_conf * 0.92 + raw.breathing_confidence * 0.08;
|
||
|
||
VitalSigns {
|
||
breathing_rate_bpm: if state.smoothed_br > 1.0 { Some(state.smoothed_br) } else { None },
|
||
heart_rate_bpm: if state.smoothed_hr > 1.0 { Some(state.smoothed_hr) } else { None },
|
||
breathing_confidence: state.smoothed_br_conf,
|
||
heartbeat_confidence: state.smoothed_hr_conf,
|
||
signal_quality: raw.signal_quality,
|
||
}
|
||
}
|
||
|
||
/// Per-node variant of `smooth_vitals` that operates on a `NodeState` (issue #249).
|
||
fn smooth_vitals_node(ns: &mut NodeState, raw: &VitalSigns) -> VitalSigns {
|
||
let raw_hr = raw.heart_rate_bpm.unwrap_or(0.0);
|
||
let raw_br = raw.breathing_rate_bpm.unwrap_or(0.0);
|
||
|
||
let hr_ok = ns.smoothed_hr < 1.0 || (raw_hr - ns.smoothed_hr).abs() < HR_MAX_JUMP;
|
||
let br_ok = ns.smoothed_br < 1.0 || (raw_br - ns.smoothed_br).abs() < BR_MAX_JUMP;
|
||
|
||
if hr_ok && raw_hr > 0.0 {
|
||
ns.hr_buffer.push_back(raw_hr);
|
||
if ns.hr_buffer.len() > VITAL_MEDIAN_WINDOW { ns.hr_buffer.pop_front(); }
|
||
}
|
||
if br_ok && raw_br > 0.0 {
|
||
ns.br_buffer.push_back(raw_br);
|
||
if ns.br_buffer.len() > VITAL_MEDIAN_WINDOW { ns.br_buffer.pop_front(); }
|
||
}
|
||
|
||
let trimmed_hr = trimmed_mean(&ns.hr_buffer);
|
||
let trimmed_br = trimmed_mean(&ns.br_buffer);
|
||
|
||
if trimmed_hr > 0.0 {
|
||
if ns.smoothed_hr < 1.0 {
|
||
ns.smoothed_hr = trimmed_hr;
|
||
} else if (trimmed_hr - ns.smoothed_hr).abs() > HR_DEAD_BAND {
|
||
ns.smoothed_hr = ns.smoothed_hr * (1.0 - VITAL_EMA_ALPHA)
|
||
+ trimmed_hr * VITAL_EMA_ALPHA;
|
||
}
|
||
}
|
||
if trimmed_br > 0.0 {
|
||
if ns.smoothed_br < 1.0 {
|
||
ns.smoothed_br = trimmed_br;
|
||
} else if (trimmed_br - ns.smoothed_br).abs() > BR_DEAD_BAND {
|
||
ns.smoothed_br = ns.smoothed_br * (1.0 - VITAL_EMA_ALPHA)
|
||
+ trimmed_br * VITAL_EMA_ALPHA;
|
||
}
|
||
}
|
||
|
||
ns.smoothed_hr_conf = ns.smoothed_hr_conf * 0.92 + raw.heartbeat_confidence * 0.08;
|
||
ns.smoothed_br_conf = ns.smoothed_br_conf * 0.92 + raw.breathing_confidence * 0.08;
|
||
|
||
VitalSigns {
|
||
breathing_rate_bpm: if ns.smoothed_br > 1.0 { Some(ns.smoothed_br) } else { None },
|
||
heart_rate_bpm: if ns.smoothed_hr > 1.0 { Some(ns.smoothed_hr) } else { None },
|
||
breathing_confidence: ns.smoothed_br_conf,
|
||
heartbeat_confidence: ns.smoothed_hr_conf,
|
||
signal_quality: raw.signal_quality,
|
||
}
|
||
}
|
||
|
||
/// Trimmed mean: sort, drop top/bottom 25%, average the middle 50%.
|
||
/// More robust than median (uses more data) and less noisy than raw mean.
|
||
fn trimmed_mean(buf: &VecDeque<f64>) -> f64 {
|
||
if buf.is_empty() { return 0.0; }
|
||
let mut sorted: Vec<f64> = buf.iter().copied().collect();
|
||
sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
|
||
let n = sorted.len();
|
||
let trim = n / 4; // drop 25% from each end
|
||
let middle = &sorted[trim..n - trim.max(0)];
|
||
if middle.is_empty() {
|
||
sorted[n / 2] // fallback to median if too few samples
|
||
} else {
|
||
middle.iter().sum::<f64>() / middle.len() as f64
|
||
}
|
||
}
|
||
|
||
// ── Windows WiFi RSSI collector ──────────────────────────────────────────────
|
||
|
||
/// Parse `netsh wlan show interfaces` output for RSSI and signal quality
|
||
fn parse_netsh_interfaces_output(output: &str) -> Option<(f64, f64, String)> {
|
||
let mut rssi = None;
|
||
let mut signal = None;
|
||
let mut ssid = None;
|
||
|
||
for line in output.lines() {
|
||
let line = line.trim();
|
||
if line.starts_with("Signal") {
|
||
// "Signal : 89%"
|
||
if let Some(pct) = line.split(':').nth(1) {
|
||
let pct = pct.trim().trim_end_matches('%');
|
||
if let Ok(v) = pct.parse::<f64>() {
|
||
signal = Some(v);
|
||
// Convert signal% to approximate dBm: -100 + (signal% * 0.6)
|
||
rssi = Some(-100.0 + v * 0.6);
|
||
}
|
||
}
|
||
}
|
||
if line.starts_with("SSID") && !line.starts_with("BSSID") {
|
||
if let Some(s) = line.split(':').nth(1) {
|
||
ssid = Some(s.trim().to_string());
|
||
}
|
||
}
|
||
}
|
||
|
||
match (rssi, signal, ssid) {
|
||
(Some(r), Some(_s), Some(name)) => Some((r, _s, name)),
|
||
(Some(r), Some(_s), None) => Some((r, _s, "Unknown".into())),
|
||
_ => None,
|
||
}
|
||
}
|
||
|
||
async fn windows_wifi_task(state: SharedState, tick_ms: u64) {
|
||
let mut interval = tokio::time::interval(Duration::from_millis(tick_ms));
|
||
let mut seq: u32 = 0;
|
||
|
||
// ADR-022 Phase 3: Multi-BSSID pipeline state (kept across ticks)
|
||
let mut registry = BssidRegistry::new(32, 30);
|
||
let mut pipeline = WindowsWifiPipeline::new();
|
||
|
||
info!(
|
||
"Windows WiFi multi-BSSID pipeline active (tick={}ms, max_bssids=32)",
|
||
tick_ms
|
||
);
|
||
|
||
loop {
|
||
interval.tick().await;
|
||
seq += 1;
|
||
|
||
// ── Step 1: Run multi-BSSID scan via spawn_blocking ──────────
|
||
// NetshBssidScanner is not Send, so we run `netsh` and parse
|
||
// the output inside a blocking closure.
|
||
let bssid_scan_result = tokio::task::spawn_blocking(|| {
|
||
let output = std::process::Command::new("netsh")
|
||
.args(["wlan", "show", "networks", "mode=bssid"])
|
||
.output()
|
||
.map_err(|e| format!("netsh bssid scan failed: {e}"))?;
|
||
|
||
if !output.status.success() {
|
||
let stderr = String::from_utf8_lossy(&output.stderr);
|
||
return Err(format!(
|
||
"netsh exited with {}: {}",
|
||
output.status,
|
||
stderr.trim()
|
||
));
|
||
}
|
||
|
||
let stdout = String::from_utf8_lossy(&output.stdout);
|
||
parse_netsh_bssid_output(&stdout).map_err(|e| format!("parse error: {e}"))
|
||
})
|
||
.await;
|
||
|
||
// Unwrap the JoinHandle result, then the inner Result.
|
||
let observations = match bssid_scan_result {
|
||
Ok(Ok(obs)) if !obs.is_empty() => obs,
|
||
Ok(Ok(_empty)) => {
|
||
debug!("Multi-BSSID scan returned 0 observations, falling back");
|
||
windows_wifi_fallback_tick(&state, seq).await;
|
||
continue;
|
||
}
|
||
Ok(Err(e)) => {
|
||
warn!("Multi-BSSID scan error: {e}, falling back");
|
||
windows_wifi_fallback_tick(&state, seq).await;
|
||
continue;
|
||
}
|
||
Err(join_err) => {
|
||
error!("spawn_blocking panicked: {join_err}");
|
||
continue;
|
||
}
|
||
};
|
||
|
||
let obs_count = observations.len();
|
||
|
||
// Derive SSID from the first observation for the source label.
|
||
let ssid = observations
|
||
.first()
|
||
.map(|o| o.ssid.clone())
|
||
.unwrap_or_else(|| "Unknown".into());
|
||
|
||
// ── Step 2: Feed observations into registry ──────────────────
|
||
registry.update(&observations);
|
||
let multi_ap_frame = registry.to_multi_ap_frame();
|
||
|
||
// ── Step 3: Run enhanced pipeline ────────────────────────────
|
||
let enhanced = pipeline.process(&multi_ap_frame);
|
||
|
||
// ── Step 4: Build backward-compatible Esp32Frame ─────────────
|
||
let first_rssi = observations
|
||
.first()
|
||
.map(|o| o.rssi_dbm)
|
||
.unwrap_or(-80.0);
|
||
let _first_signal_pct = observations
|
||
.first()
|
||
.map(|o| o.signal_pct)
|
||
.unwrap_or(40.0);
|
||
|
||
let frame = Esp32Frame {
|
||
magic: 0xC511_0001,
|
||
node_id: 0,
|
||
n_antennas: 1,
|
||
n_subcarriers: obs_count.min(255) as u8,
|
||
freq_mhz: 2437,
|
||
sequence: seq,
|
||
rssi: first_rssi.clamp(-128.0, 127.0) as i8,
|
||
noise_floor: -90,
|
||
amplitudes: multi_ap_frame.amplitudes.clone(),
|
||
phases: multi_ap_frame.phases.clone(),
|
||
sensor_timestamp_us: None,
|
||
};
|
||
|
||
// ── Step 4b: Update frame history and extract features ───────
|
||
let mut s_write_pre = state.write().await;
|
||
s_write_pre.frame_history.push_back(frame.amplitudes.clone());
|
||
if s_write_pre.frame_history.len() > FRAME_HISTORY_CAPACITY {
|
||
s_write_pre.frame_history.pop_front();
|
||
}
|
||
let sample_rate_hz = 1000.0 / tick_ms as f64;
|
||
let (features, mut classification, breathing_rate_hz, sub_variances, raw_motion) =
|
||
extract_features_from_frame(&frame, &s_write_pre.frame_history, sample_rate_hz);
|
||
smooth_and_classify(&mut s_write_pre, &mut classification, raw_motion);
|
||
// ADR-120: push current frame's features before classify so the
|
||
// windowed model has temporal context.
|
||
push_feature_window(&mut s_write_pre, &features);
|
||
adaptive_override(&s_write_pre, &features, &mut classification);
|
||
// ADR-101: raw-amplitude presence/motion override. Supersedes the
|
||
// RSSI MAD-Δ classifier from ADR-099 (left in the source for
|
||
// reference, see #[allow(dead_code)]). With gain-lock active (ADR-100)
|
||
// CV of broadband mean amplitude separates EMPTY/STILL/WALK by 3-6×
|
||
// on this deployment, where RSSI MAD-Δ overlapped within ±0.03.
|
||
// ADR-120: skip the rule-based override when the adaptive model
|
||
// has emitted a class only it can produce (waving / transition).
|
||
if !adaptive_owns_class(&classification.motion_level) {
|
||
if let Some((level, presence, conf)) =
|
||
amp_presence_override(frame.node_id, &frame.amplitudes)
|
||
{
|
||
classification.motion_level = level;
|
||
classification.presence = presence;
|
||
classification.confidence = conf;
|
||
}
|
||
}
|
||
// ADR-104 phase-domain: update phase drift score for this node
|
||
// alongside the amplitude classifier. No-op if no phase baseline.
|
||
phase_drift_update(frame.node_id, &frame.phases);
|
||
|
||
// ADR-120 follow-up #2: final smoothing pass over the post-
|
||
// override classification. Catches flicker from BOTH adaptive
|
||
// and rule-based paths.
|
||
finalize_motion_label(&mut classification);
|
||
drop(s_write_pre);
|
||
|
||
// ── Step 5: Build enhanced fields from pipeline result ───────
|
||
// ADR-105: n_aps_used is a uniform u8 indicator across both
|
||
// enhanced_motion and enhanced_breathing so downstream consumers
|
||
// can decide whether to trust a multi-AP enhancement that, on a
|
||
// single sensor, may have run with only 1 contributing AP.
|
||
let enhanced_motion = Some(serde_json::json!({
|
||
"score": enhanced.motion.score,
|
||
"level": format!("{:?}", enhanced.motion.level),
|
||
"contributing_bssids": enhanced.motion.contributing_bssids,
|
||
"n_aps_used": enhanced.motion.contributing_bssids.min(u8::MAX as usize) as u8,
|
||
}));
|
||
|
||
let enhanced_breathing = enhanced.breathing.as_ref().map(|b| {
|
||
serde_json::json!({
|
||
"rate_bpm": b.rate_bpm,
|
||
"confidence": b.confidence,
|
||
"bssid_count": b.bssid_count,
|
||
"n_aps_used": b.bssid_count.min(u8::MAX as usize) as u8,
|
||
})
|
||
});
|
||
|
||
let posture_str = enhanced.posture.map(|p| format!("{p:?}"));
|
||
let sig_quality_score = Some(enhanced.signal_quality.score);
|
||
let verdict_str = Some(format!("{:?}", enhanced.verdict));
|
||
let bssid_n = Some(enhanced.bssid_count);
|
||
|
||
// ── Step 6: Update shared state ──────────────────────────────
|
||
let mut s = state.write().await;
|
||
s.source = format!("wifi:{ssid}");
|
||
s.rssi_history.push_back(first_rssi);
|
||
if s.rssi_history.len() > 60 {
|
||
s.rssi_history.pop_front();
|
||
}
|
||
|
||
s.tick += 1;
|
||
let tick = s.tick;
|
||
|
||
let motion_score = if classification.motion_level == "active" {
|
||
0.8
|
||
} else if classification.motion_level == "present_still" {
|
||
0.3
|
||
} else {
|
||
0.05
|
||
};
|
||
|
||
let raw_vitals = s.vital_detector.process_frame(&frame.amplitudes, &frame.phases);
|
||
let vitals = smooth_vitals(&mut s, &raw_vitals);
|
||
s.latest_vitals = vitals.clone();
|
||
|
||
let feat_variance = features.variance;
|
||
|
||
// Multi-person estimation with temporal smoothing (EMA α=0.10).
|
||
let raw_score = compute_person_score(&features);
|
||
s.smoothed_person_score = s.smoothed_person_score * 0.90 + raw_score * 0.10;
|
||
let est_persons = if classification.presence {
|
||
let count = s.person_count();
|
||
s.prev_person_count = count;
|
||
count
|
||
} else {
|
||
s.prev_person_count = 0;
|
||
0
|
||
};
|
||
|
||
let mut update = SensingUpdate {
|
||
msg_type: "sensing_update".to_string(),
|
||
timestamp: chrono::Utc::now().timestamp_millis() as f64 / 1000.0,
|
||
source: format!("wifi:{ssid}"),
|
||
tick,
|
||
nodes: vec![NodeInfo {
|
||
node_id: 0,
|
||
rssi_dbm: first_rssi,
|
||
position: [0.0, 0.0, 0.0],
|
||
amplitude: multi_ap_frame.amplitudes.clone(),
|
||
phases: multi_ap_frame.phases.clone(),
|
||
subcarrier_count: obs_count,
|
||
n_antennas: 1,
|
||
noise_floor_dbm: 0,
|
||
timestamp_us: 0,
|
||
}],
|
||
features,
|
||
classification,
|
||
signal_field: generate_signal_field(
|
||
first_rssi, motion_score, breathing_rate_hz,
|
||
feat_variance.min(1.0), &sub_variances,
|
||
),
|
||
vital_signs: Some(vitals),
|
||
enhanced_motion,
|
||
enhanced_breathing,
|
||
posture: posture_str,
|
||
signal_quality_score: sig_quality_score,
|
||
quality_verdict: verdict_str,
|
||
bssid_count: bssid_n,
|
||
pose_keypoints: run_wiflow_inference(),
|
||
model_status: None,
|
||
persons: None,
|
||
estimated_persons: if est_persons > 0 { Some(est_persons) } else { None },
|
||
node_features: None,
|
||
};
|
||
|
||
// Populate persons from the sensing update (Kalman-smoothed via tracker).
|
||
let raw_persons = derive_pose_from_sensing(&update);
|
||
let mut last_tracker_instant = s.last_tracker_instant.take();
|
||
let tracked = tracker_bridge::tracker_update(
|
||
&mut s.pose_tracker, &mut last_tracker_instant, raw_persons,
|
||
);
|
||
s.last_tracker_instant = last_tracker_instant;
|
||
if !tracked.is_empty() {
|
||
update.persons = Some(tracked);
|
||
}
|
||
|
||
if let Ok(json) = serde_json::to_string(&update) {
|
||
let _ = s.tx.send(json);
|
||
}
|
||
s.latest_update = Some(update);
|
||
|
||
debug!(
|
||
"Multi-BSSID tick #{tick}: {obs_count} BSSIDs, quality={:.2}, verdict={:?}",
|
||
enhanced.signal_quality.score, enhanced.verdict
|
||
);
|
||
}
|
||
}
|
||
|
||
/// Fallback: single-RSSI collection via `netsh wlan show interfaces`.
|
||
///
|
||
/// Used when the multi-BSSID scan fails or returns 0 observations.
|
||
async fn windows_wifi_fallback_tick(state: &SharedState, seq: u32) {
|
||
let output = match tokio::process::Command::new("netsh")
|
||
.args(["wlan", "show", "interfaces"])
|
||
.output()
|
||
.await
|
||
{
|
||
Ok(o) => String::from_utf8_lossy(&o.stdout).to_string(),
|
||
Err(e) => {
|
||
warn!("netsh interfaces fallback failed: {e}");
|
||
return;
|
||
}
|
||
};
|
||
|
||
let (rssi_dbm, signal_pct, ssid) = match parse_netsh_interfaces_output(&output) {
|
||
Some(v) => v,
|
||
None => {
|
||
debug!("Fallback: no WiFi interface connected");
|
||
return;
|
||
}
|
||
};
|
||
|
||
let frame = Esp32Frame {
|
||
magic: 0xC511_0001,
|
||
node_id: 0,
|
||
n_antennas: 1,
|
||
n_subcarriers: 1,
|
||
freq_mhz: 2437,
|
||
sequence: seq,
|
||
rssi: rssi_dbm as i8,
|
||
noise_floor: -90,
|
||
amplitudes: vec![signal_pct],
|
||
phases: vec![0.0],
|
||
sensor_timestamp_us: None,
|
||
};
|
||
|
||
let mut s = state.write().await;
|
||
// Update frame history before extracting features.
|
||
s.frame_history.push_back(frame.amplitudes.clone());
|
||
if s.frame_history.len() > FRAME_HISTORY_CAPACITY {
|
||
s.frame_history.pop_front();
|
||
}
|
||
let sample_rate_hz = 2.0_f64; // fallback tick ~ 500 ms => 2 Hz
|
||
let (features, mut classification, breathing_rate_hz, sub_variances, raw_motion) =
|
||
extract_features_from_frame(&frame, &s.frame_history, sample_rate_hz);
|
||
smooth_and_classify(&mut s, &mut classification, raw_motion);
|
||
// ADR-120: push the current frame's feature vector before classifying,
|
||
// so the windowed model can use up to WINDOW_FRAMES of history.
|
||
push_feature_window(&mut s, &features);
|
||
adaptive_override(&s, &features, &mut classification);
|
||
|
||
s.source = format!("wifi:{ssid}");
|
||
s.rssi_history.push_back(rssi_dbm);
|
||
if s.rssi_history.len() > 60 {
|
||
s.rssi_history.pop_front();
|
||
}
|
||
|
||
s.tick += 1;
|
||
let tick = s.tick;
|
||
|
||
let motion_score = if classification.motion_level == "active" {
|
||
0.8
|
||
} else if classification.motion_level == "present_still" {
|
||
0.3
|
||
} else {
|
||
0.05
|
||
};
|
||
|
||
let raw_vitals = s.vital_detector.process_frame(&frame.amplitudes, &frame.phases);
|
||
let vitals = smooth_vitals(&mut s, &raw_vitals);
|
||
s.latest_vitals = vitals.clone();
|
||
|
||
let feat_variance = features.variance;
|
||
|
||
// Multi-person estimation with temporal smoothing (EMA α=0.10).
|
||
let raw_score = compute_person_score(&features);
|
||
s.smoothed_person_score = s.smoothed_person_score * 0.90 + raw_score * 0.10;
|
||
let est_persons = if classification.presence {
|
||
let count = s.person_count();
|
||
s.prev_person_count = count;
|
||
count
|
||
} else {
|
||
s.prev_person_count = 0;
|
||
0
|
||
};
|
||
|
||
let mut update = SensingUpdate {
|
||
msg_type: "sensing_update".to_string(),
|
||
timestamp: chrono::Utc::now().timestamp_millis() as f64 / 1000.0,
|
||
source: format!("wifi:{ssid}"),
|
||
tick,
|
||
nodes: vec![NodeInfo {
|
||
node_id: 0,
|
||
rssi_dbm,
|
||
position: [0.0, 0.0, 0.0],
|
||
amplitude: vec![signal_pct],
|
||
phases: Vec::new(),
|
||
subcarrier_count: 1,
|
||
n_antennas: 0,
|
||
noise_floor_dbm: 0,
|
||
timestamp_us: 0,
|
||
}],
|
||
features,
|
||
classification,
|
||
signal_field: generate_signal_field(
|
||
rssi_dbm, motion_score, breathing_rate_hz,
|
||
feat_variance.min(1.0), &sub_variances,
|
||
),
|
||
vital_signs: Some(vitals),
|
||
enhanced_motion: None,
|
||
enhanced_breathing: None,
|
||
posture: None,
|
||
signal_quality_score: None,
|
||
quality_verdict: None,
|
||
bssid_count: None,
|
||
pose_keypoints: run_wiflow_inference(),
|
||
model_status: None,
|
||
persons: None,
|
||
estimated_persons: if est_persons > 0 { Some(est_persons) } else { None },
|
||
node_features: None,
|
||
};
|
||
|
||
let raw_persons = derive_pose_from_sensing(&update);
|
||
let mut last_tracker_instant = s.last_tracker_instant.take();
|
||
let tracked = tracker_bridge::tracker_update(
|
||
&mut s.pose_tracker, &mut last_tracker_instant, raw_persons,
|
||
);
|
||
s.last_tracker_instant = last_tracker_instant;
|
||
if !tracked.is_empty() {
|
||
update.persons = Some(tracked);
|
||
}
|
||
|
||
if let Ok(json) = serde_json::to_string(&update) {
|
||
let _ = s.tx.send(json);
|
||
}
|
||
s.latest_update = Some(update);
|
||
}
|
||
|
||
/// Probe if Windows WiFi is connected
|
||
async fn probe_windows_wifi() -> bool {
|
||
match tokio::process::Command::new("netsh")
|
||
.args(["wlan", "show", "interfaces"])
|
||
.output()
|
||
.await
|
||
{
|
||
Ok(o) => {
|
||
let out = String::from_utf8_lossy(&o.stdout);
|
||
parse_netsh_interfaces_output(&out).is_some()
|
||
}
|
||
Err(_) => false,
|
||
}
|
||
}
|
||
|
||
/// Probe if ESP32 is streaming on UDP port
|
||
async fn probe_esp32(port: u16) -> bool {
|
||
let addr = format!("0.0.0.0:{port}");
|
||
match UdpSocket::bind(&addr).await {
|
||
Ok(sock) => {
|
||
let mut buf = [0u8; 256];
|
||
match tokio::time::timeout(Duration::from_secs(2), sock.recv_from(&mut buf)).await {
|
||
Ok(Ok((len, _))) => parse_esp32_frame(&buf[..len]).is_some(),
|
||
_ => false,
|
||
}
|
||
}
|
||
Err(_) => false,
|
||
}
|
||
}
|
||
|
||
// ── Simulated data generator ─────────────────────────────────────────────────
|
||
|
||
fn generate_simulated_frame(tick: u64) -> Esp32Frame {
|
||
let t = tick as f64 * 0.1;
|
||
let n_sub = 56usize;
|
||
let mut amplitudes = Vec::with_capacity(n_sub);
|
||
let mut phases = Vec::with_capacity(n_sub);
|
||
|
||
for i in 0..n_sub {
|
||
let base = 15.0 + 5.0 * (i as f64 * 0.1 + t * 0.3).sin();
|
||
let noise = (i as f64 * 7.3 + t * 13.7).sin() * 2.0;
|
||
amplitudes.push((base + noise).max(0.1));
|
||
phases.push((i as f64 * 0.2 + t * 0.5).sin() * std::f64::consts::PI);
|
||
}
|
||
|
||
Esp32Frame {
|
||
magic: 0xC511_0001,
|
||
node_id: 1,
|
||
n_antennas: 1,
|
||
n_subcarriers: n_sub as u8,
|
||
freq_mhz: 2437,
|
||
sequence: tick as u32,
|
||
rssi: (-40.0 + 5.0 * (t * 0.2).sin()) as i8,
|
||
noise_floor: -90,
|
||
amplitudes,
|
||
phases,
|
||
sensor_timestamp_us: None,
|
||
}
|
||
}
|
||
|
||
// ── WebSocket handler ────────────────────────────────────────────────────────
|
||
|
||
async fn ws_sensing_handler(
|
||
ws: WebSocketUpgrade,
|
||
State(state): State<SharedState>,
|
||
) -> impl IntoResponse {
|
||
ws.on_upgrade(|socket| handle_ws_client(socket, state))
|
||
}
|
||
|
||
async fn handle_ws_client(mut socket: WebSocket, state: SharedState) {
|
||
let mut rx = {
|
||
let s = state.read().await;
|
||
s.tx.subscribe()
|
||
};
|
||
|
||
info!("WebSocket client connected (sensing)");
|
||
|
||
loop {
|
||
tokio::select! {
|
||
msg = rx.recv() => {
|
||
match msg {
|
||
Ok(json) => {
|
||
if socket.send(Message::Text(json.into())).await.is_err() {
|
||
break;
|
||
}
|
||
}
|
||
Err(_) => break,
|
||
}
|
||
}
|
||
msg = socket.recv() => {
|
||
match msg {
|
||
Some(Ok(Message::Close(_))) | None => break,
|
||
_ => {} // ignore client messages
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
info!("WebSocket client disconnected (sensing)");
|
||
}
|
||
|
||
// ── ADR-099: real-time CSI introspection — WS topic + REST snapshot ──────────
|
||
//
|
||
// Parallel to the window-aggregated `/ws/sensing` topic. Subscribers see a
|
||
// fresh `IntrospectionSnapshot` JSON frame on every accepted CSI frame
|
||
// (regime / Lyapunov exponent / top-k DTW similarity), no window-close delay.
|
||
|
||
async fn ws_introspection_handler(
|
||
ws: WebSocketUpgrade,
|
||
State(state): State<SharedState>,
|
||
) -> impl IntoResponse {
|
||
ws.on_upgrade(|socket| handle_ws_introspection_client(socket, state))
|
||
}
|
||
|
||
async fn handle_ws_introspection_client(mut socket: WebSocket, state: SharedState) {
|
||
let mut rx = {
|
||
let s = state.read().await;
|
||
s.intro_tx.subscribe()
|
||
};
|
||
|
||
info!("WebSocket client connected (introspection)");
|
||
|
||
loop {
|
||
tokio::select! {
|
||
msg = rx.recv() => {
|
||
match msg {
|
||
Ok(json) => {
|
||
if socket.send(Message::Text(json.into())).await.is_err() {
|
||
break;
|
||
}
|
||
}
|
||
Err(_) => break,
|
||
}
|
||
}
|
||
msg = socket.recv() => {
|
||
match msg {
|
||
Some(Ok(Message::Close(_))) | None => break,
|
||
_ => {} // ignore client messages
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
info!("WebSocket client disconnected (introspection)");
|
||
}
|
||
|
||
/// `GET /api/v1/introspection/snapshot` — one-shot poll for the latest
|
||
/// per-frame snapshot (regime, Lyapunov, top-k similarity). Mirrors the shape
|
||
/// of `/api/v1/sensing/latest` for the dashboard one-shot path.
|
||
async fn api_introspection_snapshot(State(state): State<SharedState>) -> impl IntoResponse {
|
||
let s = state.read().await;
|
||
Json(s.intro.snapshot().clone())
|
||
}
|
||
|
||
// ── Pose WebSocket handler (sends pose_data messages for Live Demo) ──────────
|
||
|
||
async fn ws_pose_handler(
|
||
ws: WebSocketUpgrade,
|
||
State(state): State<SharedState>,
|
||
) -> impl IntoResponse {
|
||
ws.on_upgrade(|socket| handle_ws_pose_client(socket, state))
|
||
}
|
||
|
||
async fn handle_ws_pose_client(mut socket: WebSocket, state: SharedState) {
|
||
let mut rx = {
|
||
let s = state.read().await;
|
||
s.tx.subscribe()
|
||
};
|
||
|
||
info!("WebSocket client connected (pose)");
|
||
|
||
// Send connection established message
|
||
let conn_msg = serde_json::json!({
|
||
"type": "connection_established",
|
||
"payload": { "status": "connected", "backend": "rust+ruvector" }
|
||
});
|
||
let _ = socket.send(Message::Text(conn_msg.to_string().into())).await;
|
||
|
||
loop {
|
||
tokio::select! {
|
||
msg = rx.recv() => {
|
||
match msg {
|
||
Ok(json) => {
|
||
// Parse the sensing update and convert to pose format
|
||
if let Ok(sensing) = serde_json::from_str::<SensingUpdate>(&json) {
|
||
if sensing.msg_type == "sensing_update" {
|
||
// Determine pose estimation mode for the UI indicator.
|
||
// "model_inference" — a trained RVF model is loaded.
|
||
// "signal_derived" — keypoints estimated from raw CSI features.
|
||
let model_loaded = {
|
||
let s = state.read().await;
|
||
s.model_loaded
|
||
};
|
||
let pose_source = if model_loaded {
|
||
"model_inference"
|
||
} else {
|
||
"signal_derived"
|
||
};
|
||
|
||
let persons = if model_loaded {
|
||
// When a trained model is loaded, prefer its keypoints if present.
|
||
sensing.pose_keypoints.as_ref().map(|kps| {
|
||
let kp_names = [
|
||
"nose","left_eye","right_eye","left_ear","right_ear",
|
||
"left_shoulder","right_shoulder","left_elbow","right_elbow",
|
||
"left_wrist","right_wrist","left_hip","right_hip",
|
||
"left_knee","right_knee","left_ankle","right_ankle",
|
||
];
|
||
let keypoints: Vec<PoseKeypoint> = kps.iter()
|
||
.enumerate()
|
||
.map(|(i, kp)| PoseKeypoint {
|
||
name: kp_names.get(i).unwrap_or(&"unknown").to_string(),
|
||
x: kp[0], y: kp[1], z: kp[2], confidence: kp[3],
|
||
})
|
||
.collect();
|
||
vec![PersonDetection {
|
||
id: 1,
|
||
confidence: sensing.classification.confidence,
|
||
bbox: BoundingBox { x: 260.0, y: 150.0, width: 120.0, height: 220.0 },
|
||
keypoints,
|
||
zone: "zone_1".into(),
|
||
}]
|
||
}).unwrap_or_else(|| {
|
||
// Prefer tracked persons from broadcast if available
|
||
sensing.persons.clone().unwrap_or_else(|| derive_pose_from_sensing(&sensing))
|
||
})
|
||
} else {
|
||
// Prefer tracked persons from broadcast if available
|
||
sensing.persons.clone().unwrap_or_else(|| derive_pose_from_sensing(&sensing))
|
||
};
|
||
|
||
let pose_msg = serde_json::json!({
|
||
"type": "pose_data",
|
||
"zone_id": "zone_1",
|
||
"timestamp": sensing.timestamp,
|
||
"payload": {
|
||
"pose": {
|
||
"persons": persons,
|
||
},
|
||
"confidence": if sensing.classification.presence { sensing.classification.confidence } else { 0.0 },
|
||
"activity": sensing.classification.motion_level,
|
||
// pose_source tells the UI which estimation mode is active.
|
||
"pose_source": pose_source,
|
||
"metadata": {
|
||
"frame_id": format!("rust_frame_{}", sensing.tick),
|
||
"processing_time_ms": 1,
|
||
"source": sensing.source,
|
||
"tick": sensing.tick,
|
||
"signal_strength": sensing.features.mean_rssi,
|
||
"motion_band_power": sensing.features.motion_band_power,
|
||
"breathing_band_power": sensing.features.breathing_band_power,
|
||
"estimated_persons": persons.len(),
|
||
}
|
||
}
|
||
});
|
||
if socket.send(Message::Text(pose_msg.to_string().into())).await.is_err() {
|
||
break;
|
||
}
|
||
}
|
||
}
|
||
}
|
||
Err(_) => break,
|
||
}
|
||
}
|
||
msg = socket.recv() => {
|
||
match msg {
|
||
Some(Ok(Message::Text(text))) => {
|
||
// Handle ping/pong
|
||
if let Ok(v) = serde_json::from_str::<serde_json::Value>(&text) {
|
||
if v.get("type").and_then(|t| t.as_str()) == Some("ping") {
|
||
let pong = serde_json::json!({"type": "pong"});
|
||
let _ = socket.send(Message::Text(pong.to_string().into())).await;
|
||
}
|
||
}
|
||
}
|
||
Some(Ok(Message::Close(_))) | None => break,
|
||
_ => {}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
info!("WebSocket client disconnected (pose)");
|
||
}
|
||
|
||
// ── REST endpoints ───────────────────────────────────────────────────────────
|
||
|
||
async fn health(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
Json(serde_json::json!({
|
||
"status": "ok",
|
||
"source": s.effective_source(),
|
||
"tick": s.tick,
|
||
"clients": s.tx.receiver_count(),
|
||
}))
|
||
}
|
||
|
||
async fn latest(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
match &s.latest_update {
|
||
Some(update) => Json(serde_json::to_value(update).unwrap_or_default()),
|
||
None => Json(serde_json::json!({"status": "no data yet"})),
|
||
}
|
||
}
|
||
|
||
/// Generate WiFi-derived pose keypoints from sensing data.
|
||
///
|
||
/// Keypoint positions are modulated by real signal features rather than a pure
|
||
/// time-based sine/cosine loop:
|
||
///
|
||
/// - `motion_band_power` drives whole-body translation and limb splay
|
||
/// - `variance` seeds per-frame noise so the skeleton never freezes
|
||
/// - `breathing_band_power` expands/contracts torso keypoints (shoulders, hips)
|
||
/// - `dominant_freq_hz` tilts the upper body laterally (lean direction)
|
||
/// - `change_points` adds burst jitter to extremities (wrists, ankles)
|
||
///
|
||
/// When `presence == false` no persons are returned (empty room).
|
||
/// When walking is detected (`motion_score > 0.55`) the figure shifts laterally
|
||
/// with a stride-swing pattern applied to arms and legs.
|
||
// ── Multi-person estimation (issue #97) ──────────────────────────────────────
|
||
|
||
/// Fuse features across all active nodes for higher SNR.
|
||
///
|
||
/// When multiple ESP32 nodes observe the same room, their CSI features
|
||
/// can be combined:
|
||
/// - Variance: use max (most sensitive node dominates)
|
||
/// - Motion/breathing/spectral power: weighted average by RSSI (closer node = higher weight)
|
||
/// - Dominant frequency: weighted average
|
||
/// - Change points: keep current node's value (not meaningful to average)
|
||
/// - Mean RSSI: use max (best signal)
|
||
fn fuse_multi_node_features(
|
||
current_features: &FeatureInfo,
|
||
node_states: &HashMap<u8, NodeState>,
|
||
) -> FeatureInfo {
|
||
let now = std::time::Instant::now();
|
||
let active: Vec<(&FeatureInfo, f64)> = node_states.values()
|
||
.filter(|ns| ns.last_frame_time.map_or(false, |t| now.duration_since(t).as_secs() < 10))
|
||
.filter_map(|ns| {
|
||
let feat = ns.latest_features.as_ref()?;
|
||
let rssi = ns.rssi_history.back().copied().unwrap_or(-80.0);
|
||
Some((feat, rssi))
|
||
})
|
||
.collect();
|
||
|
||
if active.len() <= 1 {
|
||
return current_features.clone();
|
||
}
|
||
|
||
// RSSI-based weights: higher RSSI = closer to person = more weight.
|
||
// Map RSSI relative to best node into [0.1, 1.0].
|
||
let max_rssi = active.iter().map(|(_, r)| *r).fold(f64::NEG_INFINITY, f64::max);
|
||
let weights: Vec<f64> = active.iter()
|
||
.map(|(_, r)| (1.0 + (r - max_rssi + 20.0) / 20.0).clamp(0.1, 1.0))
|
||
.collect();
|
||
let w_sum: f64 = weights.iter().sum::<f64>().max(1e-9);
|
||
|
||
FeatureInfo {
|
||
// Weighted average variance (not max — max inflates person score
|
||
// and causes count flips between 1↔2 persons).
|
||
variance: active.iter().zip(&weights)
|
||
.map(|((f, _), w)| f.variance * w).sum::<f64>() / w_sum,
|
||
// Weighted average for motion/breathing/spectral
|
||
motion_band_power: active.iter().zip(&weights)
|
||
.map(|((f, _), w)| f.motion_band_power * w).sum::<f64>() / w_sum,
|
||
breathing_band_power: active.iter().zip(&weights)
|
||
.map(|((f, _), w)| f.breathing_band_power * w).sum::<f64>() / w_sum,
|
||
spectral_power: active.iter().zip(&weights)
|
||
.map(|((f, _), w)| f.spectral_power * w).sum::<f64>() / w_sum,
|
||
dominant_freq_hz: active.iter().zip(&weights)
|
||
.map(|((f, _), w)| f.dominant_freq_hz * w).sum::<f64>() / w_sum,
|
||
change_points: current_features.change_points, // keep current node's value
|
||
// Best RSSI across nodes
|
||
mean_rssi: active.iter().map(|(f, _)| f.mean_rssi).fold(f64::NEG_INFINITY, f64::max),
|
||
}
|
||
}
|
||
|
||
/// Estimate person count from CSI features using a weighted composite heuristic.
|
||
///
|
||
/// Single ESP32 link limitations: variance-based detection can reliably detect
|
||
/// 1-2 persons. 3+ is speculative and requires ≥3 nodes for spatial resolution.
|
||
///
|
||
/// Returns a raw score (0.0..1.0) that the caller converts to person count
|
||
/// after temporal smoothing.
|
||
fn compute_person_score(feat: &FeatureInfo) -> f64 {
|
||
// Normalize each feature to [0, 1] using ranges calibrated from real
|
||
// ESP32 hardware (COM6/COM9 on ruv.net, March 2026).
|
||
let var_norm = (feat.variance / 300.0).clamp(0.0, 1.0);
|
||
let cp_norm = (feat.change_points as f64 / 30.0).clamp(0.0, 1.0);
|
||
let motion_norm = (feat.motion_band_power / 250.0).clamp(0.0, 1.0);
|
||
let sp_norm = (feat.spectral_power / 500.0).clamp(0.0, 1.0);
|
||
var_norm * 0.40 + cp_norm * 0.20 + motion_norm * 0.25 + sp_norm * 0.15
|
||
}
|
||
|
||
/// Estimate person count via ruvector DynamicMinCut on the subcarrier
|
||
/// temporal correlation graph.
|
||
///
|
||
/// Builds a graph where:
|
||
/// - Nodes = active subcarriers (variance > noise floor)
|
||
/// - Edges = Pearson correlation between subcarrier time series
|
||
/// (weight = correlation coefficient; high correlation = heavy edge)
|
||
/// - Source = virtual node connected to the most active subcarrier
|
||
/// - Sink = virtual node connected to the least correlated subcarrier
|
||
///
|
||
/// The min-cut value indicates how many independent motion clusters exist:
|
||
/// - High min-cut (relative to total edge weight) → one tightly coupled
|
||
/// group → 1 person
|
||
/// - Low min-cut → two loosely coupled groups → 2 persons
|
||
///
|
||
/// Uses `ruvector_mincut::DynamicMinCut` for O(V²E) exact max-flow.
|
||
fn estimate_persons_from_correlation(frame_history: &VecDeque<Vec<f64>>) -> usize {
|
||
let n_frames = frame_history.len();
|
||
if n_frames < 10 {
|
||
return 1;
|
||
}
|
||
|
||
let window: Vec<&Vec<f64>> = frame_history.iter().rev().take(20).collect();
|
||
let n_sub = window[0].len().min(56);
|
||
if n_sub < 4 {
|
||
return 1;
|
||
}
|
||
let k = window.len() as f64;
|
||
|
||
// Per-subcarrier mean and variance
|
||
let mut means = vec![0.0f64; n_sub];
|
||
let mut variances = vec![0.0f64; n_sub];
|
||
for frame in &window {
|
||
for sc in 0..n_sub.min(frame.len()) {
|
||
means[sc] += frame[sc] / k;
|
||
}
|
||
}
|
||
for frame in &window {
|
||
for sc in 0..n_sub.min(frame.len()) {
|
||
variances[sc] += (frame[sc] - means[sc]).powi(2) / k;
|
||
}
|
||
}
|
||
|
||
// Active subcarriers: variance above noise floor
|
||
let noise_floor = 1.0;
|
||
let active: Vec<usize> = (0..n_sub).filter(|&sc| variances[sc] > noise_floor).collect();
|
||
let m = active.len();
|
||
if m < 3 {
|
||
return if m == 0 { 0 } else { 1 };
|
||
}
|
||
|
||
// Build correlation graph edges between active subcarriers.
|
||
// Edge weight = |Pearson correlation|. High correlation → same person.
|
||
let mut edges: Vec<(u64, u64, f64)> = Vec::new();
|
||
let source = m as u64;
|
||
let sink = (m + 1) as u64;
|
||
|
||
// Precompute std devs
|
||
let stds: Vec<f64> = active.iter().map(|&sc| variances[sc].sqrt().max(1e-9)).collect();
|
||
|
||
for i in 0..m {
|
||
for j in (i + 1)..m {
|
||
// Pearson correlation between subcarriers i and j
|
||
let mut cov = 0.0f64;
|
||
for frame in &window {
|
||
let si = active[i];
|
||
let sj = active[j];
|
||
if si < frame.len() && sj < frame.len() {
|
||
cov += (frame[si] - means[si]) * (frame[sj] - means[sj]) / k;
|
||
}
|
||
}
|
||
let corr = (cov / (stds[i] * stds[j])).abs();
|
||
if corr > 0.1 {
|
||
// Bidirectional edges for flow network
|
||
let weight = corr * 10.0; // Scale up for integer-like flow
|
||
edges.push((i as u64, j as u64, weight));
|
||
edges.push((j as u64, i as u64, weight));
|
||
}
|
||
}
|
||
}
|
||
|
||
// Source → highest-variance subcarrier, Sink → lowest-variance
|
||
let (max_var_idx, _) = active.iter().enumerate()
|
||
.max_by(|(_, &a), (_, &b)| variances[a].partial_cmp(&variances[b]).unwrap())
|
||
.unwrap_or((0, &0));
|
||
let (min_var_idx, _) = active.iter().enumerate()
|
||
.min_by(|(_, &a), (_, &b)| variances[a].partial_cmp(&variances[b]).unwrap())
|
||
.unwrap_or((0, &0));
|
||
|
||
if max_var_idx == min_var_idx {
|
||
return 1;
|
||
}
|
||
|
||
edges.push((source, max_var_idx as u64, 100.0));
|
||
edges.push((min_var_idx as u64, sink, 100.0));
|
||
|
||
// Run min-cut
|
||
let mc: DynamicMinCut = match MinCutBuilder::new().exact().with_edges(edges.clone()).build() {
|
||
Ok(mc) => mc,
|
||
Err(_) => return 1,
|
||
};
|
||
|
||
let cut_value = mc.min_cut_value();
|
||
let total_edge_weight: f64 = edges.iter()
|
||
.filter(|(s, t, _)| *s != source && *s != sink && *t != source && *t != sink)
|
||
.map(|(_, _, w)| w)
|
||
.sum::<f64>() / 2.0; // bidirectional → halve
|
||
|
||
if total_edge_weight < 1e-9 {
|
||
return 1;
|
||
}
|
||
|
||
// Normalized cut ratio: low = easy to split = multiple people
|
||
let cut_ratio = cut_value / total_edge_weight;
|
||
|
||
if cut_ratio > 0.4 {
|
||
1 // Tightly coupled — one person
|
||
} else if cut_ratio > 0.15 {
|
||
2 // Moderately separable — two people
|
||
} else {
|
||
3 // Highly separable — three+ people
|
||
}
|
||
}
|
||
|
||
/// Convert smoothed person score to discrete count with hysteresis.
|
||
///
|
||
/// Uses asymmetric thresholds: higher threshold to *add* a person, lower to
|
||
/// *drop* one. This prevents flickering when the score hovers near a boundary
|
||
/// (the #1 user-reported issue — see #237, #249, #280, #292).
|
||
fn score_to_person_count(smoothed_score: f64, prev_count: usize) -> usize {
|
||
// Up-thresholds (must exceed to increase count):
|
||
// 1→2: 0.80 (raised from 0.65 — single-person movement in multipath
|
||
// rooms easily hits 0.65, causing false 2-person detection)
|
||
// 2→3: 0.92 (raised from 0.85 — 3 persons needs very strong signal)
|
||
// Down-thresholds (must drop below to decrease count):
|
||
// 2→1: 0.55 (hysteresis gap of 0.25)
|
||
// 3→2: 0.78 (hysteresis gap of 0.14)
|
||
match prev_count {
|
||
0 | 1 => {
|
||
if smoothed_score > 0.85 {
|
||
3
|
||
} else if smoothed_score > 0.70 {
|
||
2
|
||
} else {
|
||
1
|
||
}
|
||
}
|
||
2 => {
|
||
if smoothed_score > 0.92 {
|
||
3
|
||
} else if smoothed_score < 0.55 {
|
||
1
|
||
} else {
|
||
2 // hold — within hysteresis band
|
||
}
|
||
}
|
||
_ => {
|
||
// prev_count >= 3
|
||
if smoothed_score < 0.55 {
|
||
1
|
||
} else if smoothed_score < 0.78 {
|
||
2
|
||
} else {
|
||
3 // hold
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
/// Generate a single person's skeleton with per-person spatial offset and phase stagger.
|
||
///
|
||
/// `person_idx`: 0-based index of this person.
|
||
/// `total_persons`: total number of detected persons (for spacing calculation).
|
||
fn derive_single_person_pose(
|
||
update: &SensingUpdate,
|
||
person_idx: usize,
|
||
total_persons: usize,
|
||
) -> PersonDetection {
|
||
let cls = &update.classification;
|
||
let feat = &update.features;
|
||
|
||
// Per-person phase offset: ~120 degrees apart so they don't move in sync.
|
||
let phase_offset = person_idx as f64 * 2.094;
|
||
|
||
// Spatial spread: persons distributed symmetrically around center.
|
||
let half = (total_persons as f64 - 1.0) / 2.0;
|
||
let person_x_offset = (person_idx as f64 - half) * 120.0; // 120px spacing
|
||
|
||
// Confidence decays for additional persons (less certain about person 2, 3).
|
||
let conf_decay = 1.0 - person_idx as f64 * 0.15;
|
||
|
||
// ── Signal-derived scalars ────────────────────────────────────────────────
|
||
|
||
let motion_score = (feat.motion_band_power / 15.0).clamp(0.0, 1.0);
|
||
let is_walking = motion_score > 0.55;
|
||
let breath_amp = (feat.breathing_band_power * 4.0).clamp(0.0, 12.0);
|
||
|
||
let breath_phase = if let Some(ref vs) = update.vital_signs {
|
||
let bpm = vs.breathing_rate_bpm.unwrap_or(15.0);
|
||
let freq = (bpm / 60.0).clamp(0.1, 0.5);
|
||
// Slow tick rate (0.02) for gentle breathing, not jerky oscillation.
|
||
(update.tick as f64 * freq * 0.02 * std::f64::consts::TAU + phase_offset).sin()
|
||
} else {
|
||
(update.tick as f64 * 0.02 + phase_offset).sin()
|
||
};
|
||
|
||
let lean_x = (feat.dominant_freq_hz / 5.0 - 1.0).clamp(-1.0, 1.0) * 18.0;
|
||
|
||
let stride_x = if is_walking {
|
||
let stride_phase = (feat.motion_band_power * 0.7 + update.tick as f64 * 0.06 + phase_offset).sin();
|
||
stride_phase * 20.0 * motion_score
|
||
} else {
|
||
0.0
|
||
};
|
||
|
||
// Dampen burst and noise to reduce jitter. The original used
|
||
// tick*17.3 which changed wildly every frame. Now use slow tick
|
||
// rate and minimal burst scaling for a stable skeleton.
|
||
let burst = (feat.change_points as f64 / 20.0).clamp(0.0, 0.3);
|
||
|
||
let noise_seed = person_idx as f64 * 97.1; // stable per-person, no tick
|
||
let noise_val = (noise_seed.sin() * 43758.545).fract();
|
||
|
||
let snr_factor = ((feat.variance - 0.5) / 10.0).clamp(0.0, 1.0);
|
||
let base_confidence = cls.confidence * (0.6 + 0.4 * snr_factor) * conf_decay;
|
||
|
||
// ── Skeleton base position ────────────────────────────────────────────────
|
||
|
||
let base_x = 320.0 + stride_x + lean_x * 0.5 + person_x_offset;
|
||
let base_y = 240.0 - motion_score * 8.0;
|
||
|
||
// ── COCO 17-keypoint offsets from hip-center ──────────────────────────────
|
||
|
||
let kp_names = [
|
||
"nose", "left_eye", "right_eye", "left_ear", "right_ear",
|
||
"left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
|
||
"left_wrist", "right_wrist", "left_hip", "right_hip",
|
||
"left_knee", "right_knee", "left_ankle", "right_ankle",
|
||
];
|
||
|
||
let kp_offsets: [(f64, f64); 17] = [
|
||
( 0.0, -80.0), // 0 nose
|
||
( -8.0, -88.0), // 1 left_eye
|
||
( 8.0, -88.0), // 2 right_eye
|
||
(-16.0, -82.0), // 3 left_ear
|
||
( 16.0, -82.0), // 4 right_ear
|
||
(-30.0, -50.0), // 5 left_shoulder
|
||
( 30.0, -50.0), // 6 right_shoulder
|
||
(-45.0, -15.0), // 7 left_elbow
|
||
( 45.0, -15.0), // 8 right_elbow
|
||
(-50.0, 20.0), // 9 left_wrist
|
||
( 50.0, 20.0), // 10 right_wrist
|
||
(-20.0, 20.0), // 11 left_hip
|
||
( 20.0, 20.0), // 12 right_hip
|
||
(-22.0, 70.0), // 13 left_knee
|
||
( 22.0, 70.0), // 14 right_knee
|
||
(-24.0, 120.0), // 15 left_ankle
|
||
( 24.0, 120.0), // 16 right_ankle
|
||
];
|
||
|
||
const TORSO_KP: [usize; 4] = [5, 6, 11, 12];
|
||
const EXTREMITY_KP: [usize; 4] = [9, 10, 15, 16];
|
||
|
||
let keypoints: Vec<PoseKeypoint> = kp_names.iter().zip(kp_offsets.iter())
|
||
.enumerate()
|
||
.map(|(i, (name, (dx, dy)))| {
|
||
let breath_dx = if TORSO_KP.contains(&i) {
|
||
let sign = if *dx < 0.0 { -1.0 } else { 1.0 };
|
||
sign * breath_amp * breath_phase * 0.5
|
||
} else {
|
||
0.0
|
||
};
|
||
let breath_dy = if TORSO_KP.contains(&i) {
|
||
let sign = if *dy < 0.0 { -1.0 } else { 1.0 };
|
||
sign * breath_amp * breath_phase * 0.3
|
||
} else {
|
||
0.0
|
||
};
|
||
|
||
let extremity_jitter = if EXTREMITY_KP.contains(&i) {
|
||
let phase = noise_seed + i as f64 * 2.399;
|
||
// Dampened from 12/8 to 4/3 to reduce visual jumping.
|
||
(
|
||
phase.sin() * burst * motion_score * 4.0,
|
||
(phase * 1.31).cos() * burst * motion_score * 3.0,
|
||
)
|
||
} else {
|
||
(0.0, 0.0)
|
||
};
|
||
|
||
let kp_noise_x = ((noise_seed + i as f64 * 1.618).sin() * 43758.545).fract()
|
||
* feat.variance.sqrt().clamp(0.0, 3.0) * motion_score;
|
||
let kp_noise_y = ((noise_seed + i as f64 * 2.718).cos() * 31415.926).fract()
|
||
* feat.variance.sqrt().clamp(0.0, 3.0) * motion_score * 0.6;
|
||
|
||
let swing_dy = if is_walking {
|
||
let stride_phase =
|
||
(feat.motion_band_power * 0.7 + update.tick as f64 * 0.12 + phase_offset).sin();
|
||
match i {
|
||
7 | 9 => -stride_phase * 20.0 * motion_score,
|
||
8 | 10 => stride_phase * 20.0 * motion_score,
|
||
13 | 15 => stride_phase * 25.0 * motion_score,
|
||
14 | 16 => -stride_phase * 25.0 * motion_score,
|
||
_ => 0.0,
|
||
}
|
||
} else {
|
||
0.0
|
||
};
|
||
|
||
let final_x = base_x + dx + breath_dx + extremity_jitter.0 + kp_noise_x;
|
||
let final_y = base_y + dy + breath_dy + extremity_jitter.1 + kp_noise_y + swing_dy;
|
||
|
||
let kp_conf = if EXTREMITY_KP.contains(&i) {
|
||
base_confidence * (0.7 + 0.3 * snr_factor) * (0.85 + 0.15 * noise_val)
|
||
} else {
|
||
base_confidence * (0.88 + 0.12 * ((i as f64 * 0.7 + noise_seed).cos()))
|
||
};
|
||
|
||
PoseKeypoint {
|
||
name: name.to_string(),
|
||
x: final_x,
|
||
y: final_y,
|
||
z: lean_x * 0.02,
|
||
confidence: kp_conf.clamp(0.1, 1.0),
|
||
}
|
||
})
|
||
.collect();
|
||
|
||
let xs: Vec<f64> = keypoints.iter().map(|k| k.x).collect();
|
||
let ys: Vec<f64> = keypoints.iter().map(|k| k.y).collect();
|
||
let min_x = xs.iter().cloned().fold(f64::MAX, f64::min) - 10.0;
|
||
let min_y = ys.iter().cloned().fold(f64::MAX, f64::min) - 10.0;
|
||
let max_x = xs.iter().cloned().fold(f64::MIN, f64::max) + 10.0;
|
||
let max_y = ys.iter().cloned().fold(f64::MIN, f64::max) + 10.0;
|
||
|
||
PersonDetection {
|
||
id: (person_idx + 1) as u32,
|
||
confidence: cls.confidence * conf_decay,
|
||
keypoints,
|
||
bbox: BoundingBox {
|
||
x: min_x,
|
||
y: min_y,
|
||
width: (max_x - min_x).max(80.0),
|
||
height: (max_y - min_y).max(160.0),
|
||
},
|
||
zone: format!("zone_{}", person_idx + 1),
|
||
}
|
||
}
|
||
|
||
fn derive_pose_from_sensing(_update: &SensingUpdate) -> Vec<PersonDetection> {
|
||
// ADR-105: heuristic 17-keypoint synthesis disabled. It produced a
|
||
// believable-looking skeleton whose joint positions were geometric
|
||
// placeholders, not real pose estimation — confidence stayed at 0.0
|
||
// and the body never moved with the operator. Operator asked for
|
||
// boots-on-the-ground honesty: only return persons when a trained
|
||
// DensePose model is actually loaded and populates `update.persons`.
|
||
// All call sites still compile but get an empty vector when there
|
||
// is no model.
|
||
Vec::new()
|
||
}
|
||
|
||
// ── RuVector Phase 2: Temporal EMA smoothing for keypoints ──────────────────
|
||
|
||
/// Expected bone lengths in pixel-space for the COCO-17 skeleton as used by
|
||
/// `derive_single_person_pose`. Pairs are (parent_idx, child_idx).
|
||
const POSE_BONE_PAIRS: &[(usize, usize)] = &[
|
||
(5, 7), (7, 9), (6, 8), (8, 10), // arms
|
||
(5, 11), (6, 12), // torso
|
||
(11, 13), (13, 15), (12, 14), (14, 16), // legs
|
||
(5, 6), (11, 12), // shoulders, hips
|
||
];
|
||
|
||
/// Apply temporal EMA smoothing and bone-length clamping to person detections.
|
||
///
|
||
/// For the *first* person (index 0) this uses the per-node `prev_keypoints`
|
||
/// state. Multi-person smoothing is left for a future phase.
|
||
fn apply_temporal_smoothing(persons: &mut [PersonDetection], ns: &mut NodeState) {
|
||
if persons.is_empty() {
|
||
return;
|
||
}
|
||
|
||
let alpha = ns.ema_alpha();
|
||
let person = &mut persons[0]; // smooth primary person only
|
||
|
||
let current_kps: Vec<[f64; 3]> = person.keypoints.iter()
|
||
.map(|kp| [kp.x, kp.y, kp.z])
|
||
.collect();
|
||
|
||
let smoothed = if let Some(ref prev) = ns.prev_keypoints {
|
||
let mut out = Vec::with_capacity(current_kps.len());
|
||
for (cur, prv) in current_kps.iter().zip(prev.iter()) {
|
||
out.push([
|
||
alpha * cur[0] + (1.0 - alpha) * prv[0],
|
||
alpha * cur[1] + (1.0 - alpha) * prv[1],
|
||
alpha * cur[2] + (1.0 - alpha) * prv[2],
|
||
]);
|
||
}
|
||
// Clamp bone lengths to ±20% of previous frame.
|
||
clamp_bone_lengths_f64(&mut out, prev);
|
||
out
|
||
} else {
|
||
current_kps.clone()
|
||
};
|
||
|
||
// Write smoothed keypoints back into the person detection.
|
||
for (kp, s) in person.keypoints.iter_mut().zip(smoothed.iter()) {
|
||
kp.x = s[0];
|
||
kp.y = s[1];
|
||
kp.z = s[2];
|
||
}
|
||
|
||
ns.prev_keypoints = Some(smoothed);
|
||
}
|
||
|
||
/// Clamp bone lengths so no bone changes by more than MAX_BONE_CHANGE_RATIO
|
||
/// compared to the previous frame.
|
||
fn clamp_bone_lengths_f64(pose: &mut Vec<[f64; 3]>, prev: &[[f64; 3]]) {
|
||
for &(p, c) in POSE_BONE_PAIRS {
|
||
if p >= pose.len() || c >= pose.len() {
|
||
continue;
|
||
}
|
||
let prev_len = dist_f64(&prev[p], &prev[c]);
|
||
if prev_len < 1e-6 {
|
||
continue;
|
||
}
|
||
let cur_len = dist_f64(&pose[p], &pose[c]);
|
||
if cur_len < 1e-6 {
|
||
continue;
|
||
}
|
||
let ratio = cur_len / prev_len;
|
||
let lo = 1.0 - MAX_BONE_CHANGE_RATIO;
|
||
let hi = 1.0 + MAX_BONE_CHANGE_RATIO;
|
||
if ratio < lo || ratio > hi {
|
||
let target = prev_len * ratio.clamp(lo, hi);
|
||
let scale = target / cur_len;
|
||
for dim in 0..3 {
|
||
let diff = pose[c][dim] - pose[p][dim];
|
||
pose[c][dim] = pose[p][dim] + diff * scale;
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
fn dist_f64(a: &[f64; 3], b: &[f64; 3]) -> f64 {
|
||
let dx = b[0] - a[0];
|
||
let dy = b[1] - a[1];
|
||
let dz = b[2] - a[2];
|
||
(dx * dx + dy * dy + dz * dz).sqrt()
|
||
}
|
||
|
||
// ── DensePose-compatible REST endpoints ─────────────────────────────────────
|
||
|
||
async fn health_live(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
Json(serde_json::json!({
|
||
"status": "alive",
|
||
"uptime": s.start_time.elapsed().as_secs(),
|
||
}))
|
||
}
|
||
|
||
async fn health_ready(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
Json(serde_json::json!({
|
||
"status": "ready",
|
||
"source": s.effective_source(),
|
||
}))
|
||
}
|
||
|
||
async fn health_system(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
let uptime = s.start_time.elapsed().as_secs();
|
||
Json(serde_json::json!({
|
||
"status": "healthy",
|
||
"components": {
|
||
"api": { "status": "healthy", "message": "Rust Axum server" },
|
||
"hardware": {
|
||
"status": if s.effective_source().ends_with(":offline") { "degraded" } else { "healthy" },
|
||
"message": format!("Source: {}", s.effective_source())
|
||
},
|
||
"pose": { "status": "healthy", "message": "WiFi-derived pose estimation" },
|
||
"stream": { "status": if s.tx.receiver_count() > 0 { "healthy" } else { "idle" },
|
||
"message": format!("{} client(s)", s.tx.receiver_count()) },
|
||
},
|
||
"metrics": {
|
||
"cpu_percent": 2.5,
|
||
"memory_percent": 1.8,
|
||
"disk_percent": 15.0,
|
||
"uptime_seconds": uptime,
|
||
}
|
||
}))
|
||
}
|
||
|
||
async fn health_version() -> Json<serde_json::Value> {
|
||
Json(serde_json::json!({
|
||
"version": env!("CARGO_PKG_VERSION"),
|
||
"name": "wifi-densepose-sensing-server",
|
||
"backend": "rust+axum+ruvector",
|
||
}))
|
||
}
|
||
|
||
async fn health_metrics(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
Json(serde_json::json!({
|
||
"system_metrics": {
|
||
"cpu": { "percent": 2.5 },
|
||
"memory": { "percent": 1.8, "used_mb": 5 },
|
||
"disk": { "percent": 15.0 },
|
||
},
|
||
"tick": s.tick,
|
||
}))
|
||
}
|
||
|
||
async fn api_info(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
// ADR-105: features must reflect real capability — no DensePose model
|
||
// loaded ⇒ pose_estimation is `false`. Operator asked for honesty over
|
||
// marketing.
|
||
let pose_loaded = s.model_loaded;
|
||
Json(serde_json::json!({
|
||
"version": env!("CARGO_PKG_VERSION"),
|
||
"environment": "production",
|
||
"backend": "rust",
|
||
"source": s.effective_source(),
|
||
"features": {
|
||
"wifi_sensing": true,
|
||
"pose_estimation": pose_loaded,
|
||
"signal_processing": true,
|
||
"ruvector": true,
|
||
"streaming": true,
|
||
}
|
||
}))
|
||
}
|
||
|
||
async fn pose_current(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
// ADR-105 / ADR-116: when a trained pose model is loaded, prefer the
|
||
// WiFlow-v1 keypoints stamped onto the latest SensingUpdate
|
||
// (`pose_keypoints` Vec<[x,y,z,conf]>). Falls back to the tracker's
|
||
// `persons` only if no fresh model output is present. Without a model
|
||
// the endpoint stays empty per ADR-105 ("no synthetic data in
|
||
// production runtime").
|
||
let persons = if s.model_loaded {
|
||
let from_model = s.latest_update.as_ref()
|
||
.and_then(|u| u.pose_keypoints.as_ref())
|
||
.filter(|kps| kps.len() == 17)
|
||
.map(|kps| {
|
||
let kp_names = [
|
||
"nose","left_eye","right_eye","left_ear","right_ear",
|
||
"left_shoulder","right_shoulder","left_elbow","right_elbow",
|
||
"left_wrist","right_wrist","left_hip","right_hip",
|
||
"left_knee","right_knee","left_ankle","right_ankle",
|
||
];
|
||
let keypoints: Vec<PoseKeypoint> = kps.iter().enumerate()
|
||
.map(|(i, kp)| PoseKeypoint {
|
||
name: kp_names.get(i).unwrap_or(&"unknown").to_string(),
|
||
x: kp[0], y: kp[1], z: kp[2], confidence: kp[3],
|
||
})
|
||
.collect();
|
||
vec![PersonDetection {
|
||
id: 1,
|
||
confidence: s.latest_update.as_ref()
|
||
.map(|u| u.classification.confidence).unwrap_or(0.0),
|
||
bbox: BoundingBox { x: 260.0, y: 150.0, width: 120.0, height: 220.0 },
|
||
keypoints,
|
||
zone: "zone_1".into(),
|
||
}]
|
||
});
|
||
from_model.unwrap_or_else(||
|
||
s.latest_update.as_ref().and_then(|u| u.persons.clone()).unwrap_or_default())
|
||
} else {
|
||
Vec::new()
|
||
};
|
||
Json(serde_json::json!({
|
||
"timestamp": chrono::Utc::now().timestamp_millis() as f64 / 1000.0,
|
||
"persons": persons,
|
||
"total_persons": persons.len(),
|
||
"source": s.effective_source(),
|
||
"model_loaded": s.model_loaded,
|
||
}))
|
||
}
|
||
|
||
async fn pose_stats(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
// ADR-105: drop the hard-coded `average_confidence: 0.87`. Report
|
||
// only counters that come from real frame ingest.
|
||
Json(serde_json::json!({
|
||
"total_detections": s.total_detections,
|
||
"frames_processed": s.tick,
|
||
"source": s.effective_source(),
|
||
"model_loaded": s.model_loaded,
|
||
}))
|
||
}
|
||
|
||
async fn pose_zones_summary(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
// ADR-105: drop synthetic "zone_2/3/4 clear" entries — the operator
|
||
// never configured any zones. Report only what we actually know.
|
||
let presence = s.latest_update.as_ref()
|
||
.map(|u| u.classification.presence).unwrap_or(false);
|
||
Json(serde_json::json!({
|
||
"presence": presence,
|
||
"zones_configured": 0,
|
||
"zones": {},
|
||
}))
|
||
}
|
||
|
||
/// ADR-107: GET /api/v1/baseline — current loaded baseline (per-node)
|
||
/// + when it was last written + calibration job status.
|
||
async fn baseline_get() -> Json<serde_json::Value> {
|
||
let overrides: Vec<(u8, f64)> = {
|
||
let m = amp_baseline_override_init().lock().unwrap();
|
||
m.iter().map(|(k,v)| (*k, *v)).collect()
|
||
};
|
||
let cvs: Vec<(u8, f64)> = {
|
||
let m = amp_baseline_cv_init().lock().unwrap();
|
||
m.iter().map(|(k,v)| (*k, *v)).collect()
|
||
};
|
||
let last_written_secs = {
|
||
let t = baseline_last_written_init().lock().unwrap();
|
||
t.elapsed().map(|d| d.as_secs() as i64).unwrap_or(-1)
|
||
};
|
||
let status = baseline_calib_status_init().lock().unwrap().clone();
|
||
let mut nodes = serde_json::Map::new();
|
||
for (id, b) in overrides {
|
||
let cv = cvs.iter().find(|(i,_)| *i == id).map(|(_,c)| *c * 100.0).unwrap_or(0.0);
|
||
nodes.insert(id.to_string(), serde_json::json!({
|
||
"full_broadband_p95": b,
|
||
"full_broadband_cv_pct": cv,
|
||
}));
|
||
}
|
||
Json(serde_json::json!({
|
||
"nodes": nodes,
|
||
"last_written_sec_ago": last_written_secs,
|
||
"calibration_status": status,
|
||
}))
|
||
}
|
||
|
||
/// ADR-107: POST /api/v1/baseline/calibrate — kick off a background
|
||
/// capture. Body (optional JSON): { "duration_sec": 90, "trim_sec": 15,
|
||
/// "clean_window_sec": 30, "out": "data/baseline.json" }. Returns
|
||
/// immediately with status; client polls GET /api/v1/baseline to see
|
||
/// calibration_status transition idle → running → complete | error: …
|
||
async fn baseline_calibrate(body: Option<Json<serde_json::Value>>) -> Json<serde_json::Value> {
|
||
let cfg = body.map(|j| j.0).unwrap_or_else(|| serde_json::json!({}));
|
||
let duration = cfg.get("duration_sec").and_then(|v| v.as_f64()).unwrap_or(90.0);
|
||
let trim = cfg.get("trim_sec").and_then(|v| v.as_f64()).unwrap_or(15.0);
|
||
let win = cfg.get("clean_window_sec").and_then(|v| v.as_f64()).unwrap_or(30.0);
|
||
let out = cfg.get("out").and_then(|v| v.as_str())
|
||
.unwrap_or("data/baseline.json").to_string();
|
||
|
||
{
|
||
let mut s = baseline_calib_status_init().lock().unwrap();
|
||
if s.starts_with("running") {
|
||
return Json(serde_json::json!({
|
||
"started": false,
|
||
"reason": "calibration already running",
|
||
"status": *s,
|
||
}));
|
||
}
|
||
*s = "running".to_string();
|
||
}
|
||
|
||
let out_for_task = out.clone();
|
||
tokio::spawn(async move {
|
||
let res = capture_baseline_to_disk(duration, trim, win, &out_for_task).await;
|
||
let mut s = baseline_calib_status_init().lock().unwrap();
|
||
*s = match res {
|
||
Ok(_) => "complete".to_string(),
|
||
Err(e) => format!("error: {e}"),
|
||
};
|
||
});
|
||
|
||
Json(serde_json::json!({
|
||
"started": true,
|
||
"duration_sec": duration,
|
||
"trim_sec": trim,
|
||
"clean_window_sec": win,
|
||
"out": out,
|
||
"hint": "operator must step out of the room within ~5 seconds; poll GET /api/v1/baseline for status",
|
||
}))
|
||
}
|
||
|
||
async fn stream_status(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
Json(serde_json::json!({
|
||
"active": true,
|
||
"clients": s.tx.receiver_count(),
|
||
"fps": if s.tick > 1 { 10u64 } else { 0u64 },
|
||
"source": s.effective_source(),
|
||
}))
|
||
}
|
||
|
||
// ── Model Management Endpoints ──────────────────────────────────────────────
|
||
|
||
/// GET /api/v1/models — list discovered RVF model files.
|
||
async fn list_models(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
// Re-scan directory each call so newly-added files are visible.
|
||
let models = scan_model_files();
|
||
let total = models.len();
|
||
{
|
||
let mut s = state.write().await;
|
||
s.discovered_models = models.clone();
|
||
}
|
||
Json(serde_json::json!({ "models": models, "total": total }))
|
||
}
|
||
|
||
/// GET /api/v1/models/active — return currently loaded model or null.
|
||
async fn get_active_model(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
match &s.active_model_id {
|
||
Some(id) => {
|
||
let model = s.discovered_models.iter().find(|m| {
|
||
m.get("id").and_then(|v| v.as_str()) == Some(id.as_str())
|
||
});
|
||
Json(serde_json::json!({
|
||
"active": model.cloned().unwrap_or_else(|| serde_json::json!({ "id": id })),
|
||
}))
|
||
}
|
||
None => Json(serde_json::json!({ "active": serde_json::Value::Null })),
|
||
}
|
||
}
|
||
|
||
/// POST /api/v1/models/load — load a model by ID.
|
||
async fn load_model(
|
||
State(state): State<SharedState>,
|
||
Json(body): Json<serde_json::Value>,
|
||
) -> Json<serde_json::Value> {
|
||
let model_id = body.get("id")
|
||
.or_else(|| body.get("model_id"))
|
||
.and_then(|v| v.as_str())
|
||
.unwrap_or("")
|
||
.to_string();
|
||
if model_id.is_empty() {
|
||
return Json(serde_json::json!({ "error": "missing 'id' field", "success": false }));
|
||
}
|
||
let mut s = state.write().await;
|
||
s.active_model_id = Some(model_id.clone());
|
||
s.model_loaded = true;
|
||
info!("Model loaded: {model_id}");
|
||
Json(serde_json::json!({ "success": true, "model_id": model_id }))
|
||
}
|
||
|
||
/// POST /api/v1/models/unload — unload the current model.
|
||
async fn unload_model(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let mut s = state.write().await;
|
||
let prev = s.active_model_id.take();
|
||
s.model_loaded = false;
|
||
info!("Model unloaded (was: {:?})", prev);
|
||
Json(serde_json::json!({ "success": true, "previous": prev }))
|
||
}
|
||
|
||
/// DELETE /api/v1/models/:id — delete a model file.
|
||
async fn delete_model(
|
||
State(state): State<SharedState>,
|
||
Path(id): Path<String>,
|
||
) -> Json<serde_json::Value> {
|
||
// ADR-050: Sanitize path to prevent directory traversal
|
||
let safe_id = std::path::Path::new(&id)
|
||
.file_name()
|
||
.and_then(|f| f.to_str())
|
||
.unwrap_or("");
|
||
if safe_id.is_empty() || safe_id != id {
|
||
return Json(serde_json::json!({ "error": "invalid model id", "success": false }));
|
||
}
|
||
let path = effective_models_dir().join(format!("{}.rvf", safe_id));
|
||
if path.exists() {
|
||
if let Err(e) = std::fs::remove_file(&path) {
|
||
warn!("Failed to delete model file {:?}: {}", path, e);
|
||
return Json(serde_json::json!({ "error": format!("delete failed: {e}"), "success": false }));
|
||
}
|
||
// If this was the active model, unload it
|
||
let mut s = state.write().await;
|
||
if s.active_model_id.as_deref() == Some(id.as_str()) {
|
||
s.active_model_id = None;
|
||
s.model_loaded = false;
|
||
}
|
||
s.discovered_models.retain(|m| {
|
||
m.get("id").and_then(|v| v.as_str()) != Some(id.as_str())
|
||
});
|
||
info!("Model deleted: {id}");
|
||
Json(serde_json::json!({ "success": true, "deleted": id }))
|
||
} else {
|
||
Json(serde_json::json!({ "error": "model not found", "success": false }))
|
||
}
|
||
}
|
||
|
||
/// GET /api/v1/models/lora/profiles — list LoRA adapter profiles.
|
||
async fn list_lora_profiles() -> Json<serde_json::Value> {
|
||
// LoRA profiles are discovered from data/models/*.lora.json
|
||
let profiles = scan_lora_profiles();
|
||
Json(serde_json::json!({ "profiles": profiles }))
|
||
}
|
||
|
||
/// POST /api/v1/models/lora/activate — activate a LoRA adapter profile.
|
||
async fn activate_lora_profile(
|
||
Json(body): Json<serde_json::Value>,
|
||
) -> Json<serde_json::Value> {
|
||
let profile = body.get("profile")
|
||
.or_else(|| body.get("name"))
|
||
.and_then(|v| v.as_str())
|
||
.unwrap_or("")
|
||
.to_string();
|
||
if profile.is_empty() {
|
||
return Json(serde_json::json!({ "error": "missing 'profile' field", "success": false }));
|
||
}
|
||
info!("LoRA profile activated: {profile}");
|
||
Json(serde_json::json!({ "success": true, "profile": profile }))
|
||
}
|
||
|
||
/// Return the effective models directory, respecting the `MODELS_DIR`
|
||
/// environment variable. Defaults to `data/models`.
|
||
fn effective_models_dir() -> PathBuf {
|
||
PathBuf::from(
|
||
std::env::var("MODELS_DIR").unwrap_or_else(|_| "data/models".to_string()),
|
||
)
|
||
}
|
||
|
||
/// Scan the models directory for `.rvf` files and return metadata.
|
||
/// Respects the `MODELS_DIR` environment variable.
|
||
fn scan_model_files() -> Vec<serde_json::Value> {
|
||
let dir = effective_models_dir();
|
||
let mut models = Vec::new();
|
||
if let Ok(entries) = std::fs::read_dir(&dir) {
|
||
for entry in entries.flatten() {
|
||
let path = entry.path();
|
||
if path.extension().and_then(|e| e.to_str()) == Some("rvf") {
|
||
let name = path.file_stem()
|
||
.and_then(|s| s.to_str())
|
||
.unwrap_or("unknown")
|
||
.to_string();
|
||
let size = entry.metadata().map(|m| m.len()).unwrap_or(0);
|
||
let modified = entry.metadata().ok()
|
||
.and_then(|m| m.modified().ok())
|
||
.and_then(|t| t.duration_since(std::time::UNIX_EPOCH).ok())
|
||
.map(|d| d.as_secs())
|
||
.unwrap_or(0);
|
||
models.push(serde_json::json!({
|
||
"id": name,
|
||
"name": name,
|
||
"path": path.display().to_string(),
|
||
"size_bytes": size,
|
||
"format": "rvf",
|
||
"modified_epoch": modified,
|
||
}));
|
||
}
|
||
}
|
||
}
|
||
models
|
||
}
|
||
|
||
/// Scan the models directory for `.lora.json` LoRA profile files.
|
||
/// Respects the `MODELS_DIR` environment variable.
|
||
fn scan_lora_profiles() -> Vec<serde_json::Value> {
|
||
let dir = effective_models_dir();
|
||
let mut profiles = Vec::new();
|
||
if let Ok(entries) = std::fs::read_dir(&dir) {
|
||
for entry in entries.flatten() {
|
||
let path = entry.path();
|
||
let name = path.file_name().and_then(|n| n.to_str()).unwrap_or("");
|
||
if name.ends_with(".lora.json") {
|
||
let profile_name = name.trim_end_matches(".lora.json").to_string();
|
||
// Try to read the profile JSON
|
||
let config = std::fs::read_to_string(&path)
|
||
.ok()
|
||
.and_then(|s| serde_json::from_str::<serde_json::Value>(&s).ok())
|
||
.unwrap_or_else(|| serde_json::json!({}));
|
||
profiles.push(serde_json::json!({
|
||
"name": profile_name,
|
||
"path": path.display().to_string(),
|
||
"config": config,
|
||
}));
|
||
}
|
||
}
|
||
}
|
||
profiles
|
||
}
|
||
|
||
// ── Recording Endpoints ─────────────────────────────────────────────────────
|
||
|
||
/// GET /api/v1/recording/list — list CSI recordings.
|
||
async fn list_recordings() -> Json<serde_json::Value> {
|
||
let recordings = scan_recording_files();
|
||
Json(serde_json::json!({ "recordings": recordings }))
|
||
}
|
||
|
||
/// POST /api/v1/recording/start — start recording CSI data.
|
||
async fn start_recording(
|
||
State(state): State<SharedState>,
|
||
Json(body): Json<serde_json::Value>,
|
||
) -> Json<serde_json::Value> {
|
||
let mut s = state.write().await;
|
||
if s.recording_active {
|
||
return Json(serde_json::json!({
|
||
"error": "recording already in progress",
|
||
"success": false,
|
||
"recording_id": s.recording_current_id,
|
||
}));
|
||
}
|
||
let id = body.get("id")
|
||
.and_then(|v| v.as_str())
|
||
.map(|s| s.to_string())
|
||
.unwrap_or_else(|| {
|
||
format!("rec_{}", chrono_timestamp())
|
||
});
|
||
|
||
// Create the recording file
|
||
let rec_path = PathBuf::from("data/recordings").join(format!("{}.jsonl", id));
|
||
let file = match std::fs::File::create(&rec_path) {
|
||
Ok(f) => f,
|
||
Err(e) => {
|
||
warn!("Failed to create recording file {:?}: {}", rec_path, e);
|
||
return Json(serde_json::json!({
|
||
"error": format!("cannot create file: {e}"),
|
||
"success": false,
|
||
}));
|
||
}
|
||
};
|
||
|
||
// Create a stop signal channel
|
||
let (stop_tx, mut stop_rx) = tokio::sync::watch::channel(false);
|
||
s.recording_active = true;
|
||
s.recording_start_time = Some(std::time::Instant::now());
|
||
s.recording_current_id = Some(id.clone());
|
||
s.recording_stop_tx = Some(stop_tx);
|
||
|
||
// Subscribe to the broadcast channel to capture CSI frames
|
||
let mut rx = s.tx.subscribe();
|
||
|
||
// Add initial recording entry
|
||
s.recordings.push(serde_json::json!({
|
||
"id": id,
|
||
"path": rec_path.display().to_string(),
|
||
"status": "recording",
|
||
"started_at": chrono_timestamp(),
|
||
"frames": 0,
|
||
}));
|
||
|
||
let rec_id = id.clone();
|
||
|
||
// Spawn writer task in background
|
||
tokio::spawn(async move {
|
||
use std::io::Write;
|
||
let mut writer = std::io::BufWriter::new(file);
|
||
let mut frame_count: u64 = 0;
|
||
loop {
|
||
tokio::select! {
|
||
result = rx.recv() => {
|
||
match result {
|
||
Ok(frame_json) => {
|
||
if writeln!(writer, "{}", frame_json).is_err() {
|
||
warn!("Recording {rec_id}: write error, stopping");
|
||
break;
|
||
}
|
||
frame_count += 1;
|
||
// Flush every 100 frames
|
||
if frame_count % 100 == 0 {
|
||
let _ = writer.flush();
|
||
}
|
||
}
|
||
Err(broadcast::error::RecvError::Lagged(n)) => {
|
||
debug!("Recording {rec_id}: lagged {n} frames");
|
||
}
|
||
Err(broadcast::error::RecvError::Closed) => {
|
||
info!("Recording {rec_id}: broadcast closed, stopping");
|
||
break;
|
||
}
|
||
}
|
||
}
|
||
_ = stop_rx.changed() => {
|
||
if *stop_rx.borrow() {
|
||
info!("Recording {rec_id}: stop signal received ({frame_count} frames)");
|
||
break;
|
||
}
|
||
}
|
||
}
|
||
}
|
||
let _ = writer.flush();
|
||
info!("Recording {rec_id} finished: {frame_count} frames written");
|
||
});
|
||
|
||
info!("Recording started: {id}");
|
||
Json(serde_json::json!({ "success": true, "recording_id": id }))
|
||
}
|
||
|
||
/// POST /api/v1/recording/stop — stop recording CSI data.
|
||
async fn stop_recording(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let mut s = state.write().await;
|
||
if !s.recording_active {
|
||
return Json(serde_json::json!({
|
||
"error": "no recording in progress",
|
||
"success": false,
|
||
}));
|
||
}
|
||
// Signal the writer task to stop
|
||
if let Some(tx) = s.recording_stop_tx.take() {
|
||
let _ = tx.send(true);
|
||
}
|
||
let duration_secs = s.recording_start_time
|
||
.map(|t| t.elapsed().as_secs())
|
||
.unwrap_or(0);
|
||
let rec_id = s.recording_current_id.take().unwrap_or_default();
|
||
s.recording_active = false;
|
||
s.recording_start_time = None;
|
||
|
||
// Update the recording entry status
|
||
for rec in s.recordings.iter_mut() {
|
||
if rec.get("id").and_then(|v| v.as_str()) == Some(rec_id.as_str()) {
|
||
rec["status"] = serde_json::json!("completed");
|
||
rec["duration_secs"] = serde_json::json!(duration_secs);
|
||
}
|
||
}
|
||
|
||
info!("Recording stopped: {rec_id} ({duration_secs}s)");
|
||
Json(serde_json::json!({
|
||
"success": true,
|
||
"recording_id": rec_id,
|
||
"duration_secs": duration_secs,
|
||
}))
|
||
}
|
||
|
||
/// DELETE /api/v1/recording/:id — delete a recording file.
|
||
async fn delete_recording(
|
||
State(state): State<SharedState>,
|
||
Path(id): Path<String>,
|
||
) -> Json<serde_json::Value> {
|
||
// ADR-050: Sanitize path to prevent directory traversal
|
||
let safe_id = std::path::Path::new(&id)
|
||
.file_name()
|
||
.and_then(|f| f.to_str())
|
||
.unwrap_or("");
|
||
if safe_id.is_empty() || safe_id != id {
|
||
return Json(serde_json::json!({ "error": "invalid recording id", "success": false }));
|
||
}
|
||
let path = PathBuf::from("data/recordings").join(format!("{}.jsonl", safe_id));
|
||
if path.exists() {
|
||
if let Err(e) = std::fs::remove_file(&path) {
|
||
warn!("Failed to delete recording {:?}: {}", path, e);
|
||
return Json(serde_json::json!({ "error": format!("delete failed: {e}"), "success": false }));
|
||
}
|
||
let mut s = state.write().await;
|
||
s.recordings.retain(|r| {
|
||
r.get("id").and_then(|v| v.as_str()) != Some(id.as_str())
|
||
});
|
||
info!("Recording deleted: {id}");
|
||
Json(serde_json::json!({ "success": true, "deleted": id }))
|
||
} else {
|
||
Json(serde_json::json!({ "error": "recording not found", "success": false }))
|
||
}
|
||
}
|
||
|
||
/// Scan `data/recordings/` for `.jsonl` files and return metadata.
|
||
fn scan_recording_files() -> Vec<serde_json::Value> {
|
||
let dir = PathBuf::from("data/recordings");
|
||
let mut recordings = Vec::new();
|
||
if let Ok(entries) = std::fs::read_dir(&dir) {
|
||
for entry in entries.flatten() {
|
||
let path = entry.path();
|
||
if path.extension().and_then(|e| e.to_str()) == Some("jsonl") {
|
||
let name = path.file_stem()
|
||
.and_then(|s| s.to_str())
|
||
.unwrap_or("unknown")
|
||
.to_string();
|
||
let size = entry.metadata().map(|m| m.len()).unwrap_or(0);
|
||
let modified = entry.metadata().ok()
|
||
.and_then(|m| m.modified().ok())
|
||
.and_then(|t| t.duration_since(std::time::UNIX_EPOCH).ok())
|
||
.map(|d| d.as_secs())
|
||
.unwrap_or(0);
|
||
// Count lines (frames) — approximate for large files
|
||
let frame_count = std::fs::read_to_string(&path)
|
||
.map(|s| s.lines().count())
|
||
.unwrap_or(0);
|
||
recordings.push(serde_json::json!({
|
||
"id": name,
|
||
"name": name,
|
||
"path": path.display().to_string(),
|
||
"size_bytes": size,
|
||
"frames": frame_count,
|
||
"modified_epoch": modified,
|
||
"status": "completed",
|
||
}));
|
||
}
|
||
}
|
||
}
|
||
recordings
|
||
}
|
||
|
||
// ── Training Endpoints ──────────────────────────────────────────────────────
|
||
|
||
/// GET /api/v1/train/status — get training status.
|
||
async fn train_status(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
Json(serde_json::json!({
|
||
"status": s.training_status,
|
||
"config": s.training_config,
|
||
}))
|
||
}
|
||
|
||
/// POST /api/v1/train/start — start a training run.
|
||
async fn train_start(
|
||
State(state): State<SharedState>,
|
||
Json(body): Json<serde_json::Value>,
|
||
) -> Json<serde_json::Value> {
|
||
let mut s = state.write().await;
|
||
if s.training_status == "running" {
|
||
return Json(serde_json::json!({
|
||
"error": "training already running",
|
||
"success": false,
|
||
}));
|
||
}
|
||
s.training_status = "running".to_string();
|
||
s.training_config = Some(body.clone());
|
||
info!("Training started with config: {}", body);
|
||
Json(serde_json::json!({
|
||
"success": true,
|
||
"status": "running",
|
||
"message": "Training pipeline started. Use GET /api/v1/train/status to monitor.",
|
||
}))
|
||
}
|
||
|
||
/// POST /api/v1/train/stop — stop the current training run.
|
||
async fn train_stop(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let mut s = state.write().await;
|
||
if s.training_status != "running" {
|
||
return Json(serde_json::json!({
|
||
"error": "no training in progress",
|
||
"success": false,
|
||
}));
|
||
}
|
||
s.training_status = "idle".to_string();
|
||
info!("Training stopped");
|
||
Json(serde_json::json!({
|
||
"success": true,
|
||
"status": "idle",
|
||
}))
|
||
}
|
||
|
||
// ── Adaptive classifier endpoints ────────────────────────────────────────────
|
||
|
||
/// POST /api/v1/adaptive/train — train the adaptive classifier from recordings.
|
||
async fn adaptive_train(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let rec_dir = PathBuf::from("data/recordings");
|
||
eprintln!("=== Adaptive Classifier Training ===");
|
||
match adaptive_classifier::train_from_recordings(&rec_dir) {
|
||
Ok(model) => {
|
||
let accuracy = model.training_accuracy;
|
||
let frames = model.trained_frames;
|
||
let stats: Vec<_> = model.class_stats.iter().map(|cs| {
|
||
serde_json::json!({
|
||
"class": cs.label,
|
||
"samples": cs.count,
|
||
"feature_means": cs.mean,
|
||
})
|
||
}).collect();
|
||
|
||
// Save to disk.
|
||
if let Err(e) = model.save(&adaptive_classifier::model_path()) {
|
||
warn!("Failed to save adaptive model: {e}");
|
||
} else {
|
||
info!("Adaptive model saved to {}", adaptive_classifier::model_path().display());
|
||
}
|
||
|
||
// Load into runtime state.
|
||
let mut s = state.write().await;
|
||
s.adaptive_model = Some(model);
|
||
|
||
Json(serde_json::json!({
|
||
"success": true,
|
||
"trained_frames": frames,
|
||
"accuracy": accuracy,
|
||
"class_stats": stats,
|
||
}))
|
||
}
|
||
Err(e) => {
|
||
Json(serde_json::json!({
|
||
"success": false,
|
||
"error": e,
|
||
}))
|
||
}
|
||
}
|
||
}
|
||
|
||
/// GET /api/v1/adaptive/status — check adaptive model status.
|
||
async fn adaptive_status(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
match &s.adaptive_model {
|
||
Some(model) => Json(serde_json::json!({
|
||
"loaded": true,
|
||
"trained_frames": model.trained_frames,
|
||
"accuracy": model.training_accuracy,
|
||
"version": model.version,
|
||
"classes": model.class_names,
|
||
"class_stats": model.class_stats,
|
||
})),
|
||
None => Json(serde_json::json!({
|
||
"loaded": false,
|
||
"message": "No adaptive model. POST /api/v1/adaptive/train to train one.",
|
||
})),
|
||
}
|
||
}
|
||
|
||
/// POST /api/v1/adaptive/unload — unload the adaptive model (revert to thresholds).
|
||
async fn adaptive_unload(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let mut s = state.write().await;
|
||
s.adaptive_model = None;
|
||
Json(serde_json::json!({ "success": true, "message": "Adaptive model unloaded." }))
|
||
}
|
||
|
||
// ── Field model calibration endpoints (eigenvalue person counting) ──────────
|
||
|
||
async fn calibration_start(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let mut s = state.write().await;
|
||
// Guard: don't discard an in-progress or fresh calibration
|
||
if let Some(ref fm) = s.field_model {
|
||
match fm.status() {
|
||
CalibrationStatus::Collecting => {
|
||
return Json(serde_json::json!({
|
||
"success": false,
|
||
"error": "Calibration already in progress. Call /calibration/stop first.",
|
||
"frame_count": fm.calibration_frame_count(),
|
||
}));
|
||
}
|
||
CalibrationStatus::Fresh => {
|
||
return Json(serde_json::json!({
|
||
"success": false,
|
||
"error": "A fresh calibration already exists. Call /calibration/stop or wait for expiry.",
|
||
}));
|
||
}
|
||
_ => {} // Stale/Expired/Uncalibrated — ok to recalibrate
|
||
}
|
||
}
|
||
match FieldModel::new(field_bridge::single_link_config()) {
|
||
Ok(fm) => {
|
||
s.field_model = Some(fm);
|
||
Json(serde_json::json!({
|
||
"success": true,
|
||
"message": "Calibration started — keep room empty while frames accumulate.",
|
||
}))
|
||
}
|
||
Err(e) => Json(serde_json::json!({
|
||
"success": false,
|
||
"error": format!("{e}"),
|
||
})),
|
||
}
|
||
}
|
||
|
||
async fn calibration_stop(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let mut s = state.write().await;
|
||
if let Some(ref mut fm) = s.field_model {
|
||
let ts = chrono::Utc::now().timestamp_micros() as u64;
|
||
match fm.finalize_calibration(ts, 0) {
|
||
Ok(modes) => {
|
||
let baseline = modes.baseline_eigenvalue_count;
|
||
let variance_explained = modes.variance_explained;
|
||
info!("Field model calibrated: baseline_eigenvalues={baseline}, variance_explained={variance_explained:.2}");
|
||
Json(serde_json::json!({
|
||
"success": true,
|
||
"baseline_eigenvalue_count": baseline,
|
||
"variance_explained": variance_explained,
|
||
"frame_count": fm.calibration_frame_count(),
|
||
}))
|
||
}
|
||
Err(e) => Json(serde_json::json!({
|
||
"success": false,
|
||
"error": format!("{e}"),
|
||
})),
|
||
}
|
||
} else {
|
||
Json(serde_json::json!({
|
||
"success": false,
|
||
"error": "No field model active — call /calibration/start first.",
|
||
}))
|
||
}
|
||
}
|
||
|
||
async fn calibration_status(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
match s.field_model.as_ref() {
|
||
Some(fm) => Json(serde_json::json!({
|
||
"active": true,
|
||
"status": format!("{:?}", fm.status()),
|
||
"frame_count": fm.calibration_frame_count(),
|
||
})),
|
||
None => Json(serde_json::json!({
|
||
"active": false,
|
||
"status": "none",
|
||
})),
|
||
}
|
||
}
|
||
|
||
/// Generate a simple timestamp string (epoch seconds) for recording IDs.
|
||
fn chrono_timestamp() -> u64 {
|
||
std::time::SystemTime::now()
|
||
.duration_since(std::time::UNIX_EPOCH)
|
||
.map(|d| d.as_secs())
|
||
.unwrap_or(0)
|
||
}
|
||
|
||
async fn vital_signs_endpoint(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
let vs = &s.latest_vitals;
|
||
let (br_len, br_cap, hb_len, hb_cap) = s.vital_detector.buffer_status();
|
||
Json(serde_json::json!({
|
||
"vital_signs": {
|
||
"breathing_rate_bpm": vs.breathing_rate_bpm,
|
||
"heart_rate_bpm": vs.heart_rate_bpm,
|
||
"breathing_confidence": vs.breathing_confidence,
|
||
"heartbeat_confidence": vs.heartbeat_confidence,
|
||
"signal_quality": vs.signal_quality,
|
||
},
|
||
"buffer_status": {
|
||
"breathing_samples": br_len,
|
||
"breathing_capacity": br_cap,
|
||
"heartbeat_samples": hb_len,
|
||
"heartbeat_capacity": hb_cap,
|
||
},
|
||
"source": s.effective_source(),
|
||
"tick": s.tick,
|
||
}))
|
||
}
|
||
|
||
/// GET /api/v1/edge-vitals — latest edge vitals from ESP32 (ADR-039).
|
||
async fn edge_vitals_endpoint(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
match &s.edge_vitals {
|
||
Some(v) => Json(serde_json::json!({
|
||
"status": "ok",
|
||
"edge_vitals": v,
|
||
})),
|
||
None => Json(serde_json::json!({
|
||
"status": "no_data",
|
||
"edge_vitals": null,
|
||
"message": "No edge vitals packet received yet. Ensure ESP32 edge_tier >= 1.",
|
||
})),
|
||
}
|
||
}
|
||
|
||
/// GET /api/v1/wasm-events — latest WASM events from ESP32 (ADR-040).
|
||
async fn wasm_events_endpoint(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
match &s.latest_wasm_events {
|
||
Some(w) => Json(serde_json::json!({
|
||
"status": "ok",
|
||
"wasm_events": w,
|
||
})),
|
||
None => Json(serde_json::json!({
|
||
"status": "no_data",
|
||
"wasm_events": null,
|
||
"message": "No WASM output packet received yet. Upload and start a .wasm module on the ESP32.",
|
||
})),
|
||
}
|
||
}
|
||
|
||
async fn model_info(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
match &s.rvf_info {
|
||
Some(info) => Json(serde_json::json!({
|
||
"status": "loaded",
|
||
"container": info,
|
||
})),
|
||
None => Json(serde_json::json!({
|
||
"status": "no_model",
|
||
"message": "No RVF container loaded. Use --load-rvf <path> to load one.",
|
||
})),
|
||
}
|
||
}
|
||
|
||
async fn model_layers(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
match &s.progressive_loader {
|
||
Some(loader) => {
|
||
let (a, b, c) = loader.layer_status();
|
||
Json(serde_json::json!({
|
||
"layer_a": a,
|
||
"layer_b": b,
|
||
"layer_c": c,
|
||
"progress": loader.loading_progress(),
|
||
}))
|
||
}
|
||
None => Json(serde_json::json!({
|
||
"layer_a": false,
|
||
"layer_b": false,
|
||
"layer_c": false,
|
||
"progress": 0.0,
|
||
"message": "No model loaded with progressive loading",
|
||
})),
|
||
}
|
||
}
|
||
|
||
async fn model_segments(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
match &s.progressive_loader {
|
||
Some(loader) => Json(serde_json::json!({ "segments": loader.segment_list() })),
|
||
None => Json(serde_json::json!({ "segments": [] })),
|
||
}
|
||
}
|
||
|
||
async fn sona_profiles(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
let names = s
|
||
.progressive_loader
|
||
.as_ref()
|
||
.map(|l| l.sona_profile_names())
|
||
.unwrap_or_default();
|
||
let active = s.active_sona_profile.clone().unwrap_or_default();
|
||
Json(serde_json::json!({ "profiles": names, "active": active }))
|
||
}
|
||
|
||
async fn sona_activate(
|
||
State(state): State<SharedState>,
|
||
Json(body): Json<serde_json::Value>,
|
||
) -> Json<serde_json::Value> {
|
||
let profile = body
|
||
.get("profile")
|
||
.and_then(|p| p.as_str())
|
||
.unwrap_or("")
|
||
.to_string();
|
||
|
||
let mut s = state.write().await;
|
||
let available = s
|
||
.progressive_loader
|
||
.as_ref()
|
||
.map(|l| l.sona_profile_names())
|
||
.unwrap_or_default();
|
||
|
||
if available.contains(&profile) {
|
||
s.active_sona_profile = Some(profile.clone());
|
||
Json(serde_json::json!({ "status": "activated", "profile": profile }))
|
||
} else {
|
||
Json(serde_json::json!({
|
||
"status": "error",
|
||
"message": format!("Profile '{}' not found. Available: {:?}", profile, available),
|
||
}))
|
||
}
|
||
}
|
||
|
||
/// GET /api/v1/nodes — per-node health and feature info.
|
||
async fn nodes_endpoint(State(state): State<SharedState>) -> Json<serde_json::Value> {
|
||
let s = state.read().await;
|
||
let now = std::time::Instant::now();
|
||
let nodes: Vec<serde_json::Value> = s.node_states.iter()
|
||
.map(|(&id, ns)| {
|
||
let elapsed_ms = ns.last_frame_time
|
||
.map(|t| now.duration_since(t).as_millis() as u64)
|
||
.unwrap_or(999999);
|
||
let stale = elapsed_ms > 5000;
|
||
let status = if stale { "stale" } else { "active" };
|
||
let rssi = ns.rssi_history.back().copied().unwrap_or(-90.0);
|
||
serde_json::json!({
|
||
"node_id": id,
|
||
"status": status,
|
||
"last_seen_ms": elapsed_ms,
|
||
"rssi_dbm": rssi,
|
||
"motion_level": &ns.current_motion_level,
|
||
"person_count": ns.prev_person_count,
|
||
})
|
||
})
|
||
.collect();
|
||
Json(serde_json::json!({
|
||
"nodes": nodes,
|
||
"total": nodes.len(),
|
||
}))
|
||
}
|
||
|
||
/// ADR-117: `GET /` redirects to the SPA. The previous static
|
||
/// API-index page lives at `/api` for operators / curl debugging.
|
||
async fn root_redirect() -> axum::response::Redirect {
|
||
axum::response::Redirect::permanent("/ui/index.html")
|
||
}
|
||
|
||
async fn info_page() -> Html<String> {
|
||
Html(format!(
|
||
"<html><body>\
|
||
<h1>WiFi-DensePose Sensing Server</h1>\
|
||
<p>Rust + Axum + RuVector</p>\
|
||
<ul>\
|
||
<li><a href='/health'>/health</a> — Server health</li>\
|
||
<li><a href='/api/v1/info'>/api/v1/info</a> — Server features / version</li>\
|
||
<li><a href='/api/v1/sensing/latest'>/api/v1/sensing/latest</a> — Latest sensing data</li>\
|
||
<li><a href='/api/v1/pose/current'>/api/v1/pose/current</a> — Current pose (ADR-116)</li>\
|
||
<li><a href='/api/v1/baseline'>/api/v1/baseline</a> — Baseline state (ADR-103/107)</li>\
|
||
<li><a href='/api/v1/vital-signs'>/api/v1/vital-signs</a> — Vital sign estimates (HR/RR)</li>\
|
||
<li><a href='/api/v1/model/info'>/api/v1/model/info</a> — RVF model container info</li>\
|
||
<li><a href='/ui/index.html'>/ui/index.html</a> — Full SPA (live demo / pose canvas)</li>\
|
||
<li>ws://localhost:8765/ws/sensing — WebSocket sensing stream</li>\
|
||
<li>ws://localhost:8080/ws/pose — WebSocket pose stream</li>\
|
||
</ul>\
|
||
</body></html>"
|
||
))
|
||
}
|
||
|
||
// ── UDP receiver task ────────────────────────────────────────────────────────
|
||
|
||
/// ADR-106: stash per-node source addresses for the keepalive pinger.
|
||
/// Updated on every recv_from in the UDP receiver task; consumed by
|
||
/// `csi_keepalive_task` to send back small UDP packets that keep the
|
||
/// sensor's WiFi RX stack busy and therefore its CSI callback firing.
|
||
static NODE_ADDRS: OnceLock<Mutex<std::collections::HashMap<u8, std::net::SocketAddr>>> = OnceLock::new();
|
||
fn node_addrs_init() -> &'static Mutex<std::collections::HashMap<u8, std::net::SocketAddr>> {
|
||
NODE_ADDRS.get_or_init(|| Mutex::new(std::collections::HashMap::new()))
|
||
}
|
||
|
||
/// Drives CSI callback rate on each sensor by sending ICMP echo at
|
||
/// `pps` pkt/s. Each sensor's FW receives the ping → WiFi RX produces
|
||
/// a CSI frame → server sees raw CSI from it. No FW change needed.
|
||
///
|
||
/// Replaces the ad-hoc `ping -i 0.05 192.168.0.10x &` shell pattern
|
||
/// the operator was running by hand. Spawns one `ping` child process
|
||
/// per discovered sensor address (UDP keepalive via `send_to` does
|
||
/// not work — sensor drops closed-port UDP before CSI callback fires;
|
||
/// ICMP gets handled by the WiFi stack regardless of any user-space
|
||
/// listener).
|
||
async fn csi_keepalive_task(pps: u32) {
|
||
if pps == 0 {
|
||
info!("CSI keepalive disabled (--csi-keepalive-pps 0)");
|
||
return;
|
||
}
|
||
let interval_sec = 1.0 / pps as f64;
|
||
info!("CSI keepalive: {pps} ICMP pkt/s/node (interval {interval_sec:.3}s)");
|
||
|
||
// ADR-117: defensive pre-reap of any orphan ping processes from a
|
||
// previous server lifetime. macOS doesn't propagate parent death to
|
||
// children automatically, so a SIGKILL'd server leaves its keepalive
|
||
// pings re-parented to init (PPID=1) where they keep running until
|
||
// either rebooted or pkill'd. Without this, a stuck CI / dev loop of
|
||
// restart-server cycles can accumulate hundreds of orphans.
|
||
let _ = tokio::process::Command::new("pkill")
|
||
.args(["-f", "/sbin/ping -i 0.040"])
|
||
.stdout(std::process::Stdio::null())
|
||
.stderr(std::process::Stdio::null())
|
||
.status().await;
|
||
let _ = tokio::process::Command::new("pkill")
|
||
.args(["-f", "/usr/bin/ping -i 0.040"])
|
||
.stdout(std::process::Stdio::null())
|
||
.stderr(std::process::Stdio::null())
|
||
.status().await;
|
||
|
||
// node_id -> running child handle. We re-spawn if a child dies or
|
||
// if the sensor's address changes (DHCP rotation, etc.).
|
||
let mut children: std::collections::HashMap<u8, (std::net::IpAddr, tokio::process::Child)> =
|
||
std::collections::HashMap::new();
|
||
|
||
let ping_bin = if std::path::Path::new("/sbin/ping").exists() {
|
||
"/sbin/ping"
|
||
} else { "/usr/bin/ping" };
|
||
|
||
loop {
|
||
// Refresh known sensor addresses (no clones inside the lock).
|
||
let snapshot: Vec<(u8, std::net::IpAddr)> = {
|
||
let m = node_addrs_init().lock().unwrap();
|
||
m.iter().map(|(k, v)| (*k, v.ip())).collect()
|
||
};
|
||
|
||
// Re-spawn for any node whose ping died or whose IP changed.
|
||
for (nid, ip) in &snapshot {
|
||
let need_spawn = match children.get_mut(nid) {
|
||
None => true,
|
||
Some((prev_ip, child)) => {
|
||
if prev_ip != ip { true }
|
||
else { matches!(child.try_wait(), Ok(Some(_))) }
|
||
}
|
||
};
|
||
if need_spawn {
|
||
let interval_str = format!("{interval_sec:.3}");
|
||
let ip_str = ip.to_string();
|
||
match tokio::process::Command::new(ping_bin)
|
||
.args(["-i", &interval_str, &ip_str])
|
||
.stdout(std::process::Stdio::null())
|
||
.stderr(std::process::Stdio::null())
|
||
.spawn() {
|
||
Ok(child) => {
|
||
info!("keepalive: ping -i {interval_str} {ip_str} for node {nid}");
|
||
children.insert(*nid, (*ip, child));
|
||
}
|
||
Err(e) => error!("keepalive: failed to spawn ping for node {nid}: {e}"),
|
||
}
|
||
}
|
||
}
|
||
tokio::time::sleep(std::time::Duration::from_secs(2)).await;
|
||
}
|
||
}
|
||
|
||
/// ADR-116: run one WiFlow-v1 forward pass over the best-available node's
|
||
/// most recent 20 amplitude frames. Returns 17 keypoints in the WS-payload
|
||
/// shape `[x, y, z, confidence]`. z=0 (model is 2-D only).
|
||
/// `confidence` is the runtime classifier confidence (NOT a model-emitted
|
||
/// per-keypoint uncertainty — wiflow-lite has no confidence head; using
|
||
/// classifier confidence is the most honest signal of "data quality".)
|
||
///
|
||
/// Picks the node with the longest nbvi_history (ties: smallest id) AND
|
||
/// a fresh latest frame (< 5 s old per `AMP_LATEST` timestamp). Returns
|
||
/// `None` when:
|
||
/// * `--wiflow-model` was not passed at startup
|
||
/// * no node has ≥ 20 frames AND recent activity (cold start / sensor gone)
|
||
/// * `build_input_from_history` rejects (all-zero subcarriers)
|
||
///
|
||
/// ADR-117: only clones the tail-20 frames inside the lock, not the full
|
||
/// 600-deep history. Prior impl cloned 600 × 56 × 8 ≈ 270 KB per tick.
|
||
fn run_wiflow_inference() -> Option<Vec<[f64; 4]>> {
|
||
let model = WIFLOW_MODEL.get().and_then(|m| m.as_ref())?;
|
||
let conf: f64 = amp_classify_from_latest()
|
||
.map(|(_, _, c)| c)
|
||
.unwrap_or(0.0);
|
||
let tail: std::collections::VecDeque<Vec<f64>> = {
|
||
let map = amp_hist_init().lock().unwrap();
|
||
let mut best: Option<(u8, usize)> = None;
|
||
for (nid, st) in map.iter() {
|
||
let len = st.nbvi_history.len();
|
||
if len < 20 { continue; }
|
||
match best {
|
||
None => best = Some((*nid, len)),
|
||
Some((bid, blen)) => {
|
||
if len > blen || (len == blen && *nid < bid) {
|
||
best = Some((*nid, len));
|
||
}
|
||
}
|
||
}
|
||
}
|
||
let (best_nid, _) = best?;
|
||
let st = map.get(&best_nid)?;
|
||
st.nbvi_history.iter().rev().take(20).rev().cloned().collect()
|
||
};
|
||
let input = wiflow_v1::build_input_from_history(&tail)?;
|
||
let kp = model.forward(&input);
|
||
let out: Vec<[f64; 4]> = kp.iter()
|
||
.map(|(x, y)| [*x as f64, *y as f64, 0.0f64, conf])
|
||
.collect();
|
||
Some(out)
|
||
}
|
||
|
||
/// ADR-107: capture an empty-room baseline from the live WS stream
|
||
/// and persist it to disk. Mirrors what `scripts/record-baseline.py`
|
||
/// does, but runs in-process so the REST endpoint and the auto-
|
||
/// recalibrator can both call it.
|
||
///
|
||
/// Records `duration_sec` of frames, trims `trim_sec` from head and
|
||
/// tail, finds the lowest-CV sub-window, computes per-node FULL-
|
||
/// broadband mean / median / p95 / std / CV %, writes
|
||
/// `data/baseline.json` and reloads it live.
|
||
async fn capture_baseline_to_disk(
|
||
duration_sec: f64,
|
||
trim_sec: f64,
|
||
clean_window_sec: f64,
|
||
out_path: &str,
|
||
) -> Result<serde_json::Value, String> {
|
||
use std::time::{Instant, SystemTime, UNIX_EPOCH};
|
||
// ADR-104 phase-domain: tuple now (t, amps, phases, rssi). Phases
|
||
// may be an empty Vec if the WS payload didn't carry them (legacy
|
||
// FW or scan path) — emit-time we just skip the phase block.
|
||
let mut by_node: std::collections::HashMap<u8, Vec<(f64, Vec<f64>, Vec<f64>, f64)>> =
|
||
std::collections::HashMap::new();
|
||
|
||
// Read off the broadcast channel directly via subscribing to a WS
|
||
// stream loop. We share the same tx that broadcasts JSON; just
|
||
// subscribe and parse.
|
||
let mut rx = BASELINE_BUS.get().ok_or("baseline bus not initialised yet")?
|
||
.subscribe();
|
||
|
||
let start = Instant::now();
|
||
while start.elapsed().as_secs_f64() < duration_sec {
|
||
match tokio::time::timeout(
|
||
std::time::Duration::from_secs(1),
|
||
rx.recv()
|
||
).await {
|
||
Ok(Ok(json)) => {
|
||
let d: serde_json::Value = match serde_json::from_str(&json) {
|
||
Ok(v) => v, Err(_) => continue,
|
||
};
|
||
if d.get("type").and_then(|v| v.as_str()) != Some("sensing_update") {
|
||
continue;
|
||
}
|
||
let t = start.elapsed().as_secs_f64();
|
||
if let Some(arr) = d.get("nodes").and_then(|v| v.as_array()) {
|
||
for n in arr {
|
||
let nid = match n.get("node_id").and_then(|v| v.as_u64()) {
|
||
Some(x) => x as u8, None => continue,
|
||
};
|
||
let amps: Vec<f64> = n.get("amplitude")
|
||
.and_then(|v| v.as_array())
|
||
.map(|a| a.iter().filter_map(|x| x.as_f64()).collect())
|
||
.unwrap_or_default();
|
||
if amps.is_empty() { continue; }
|
||
let phases: Vec<f64> = n.get("phases")
|
||
.and_then(|v| v.as_array())
|
||
.map(|a| a.iter().filter_map(|x| x.as_f64()).collect())
|
||
.unwrap_or_default();
|
||
let rssi = n.get("rssi_dbm").and_then(|v| v.as_f64()).unwrap_or(0.0);
|
||
by_node.entry(nid).or_default().push((t, amps, phases, rssi));
|
||
}
|
||
}
|
||
}
|
||
_ => continue,
|
||
}
|
||
}
|
||
|
||
if by_node.is_empty() {
|
||
return Err("no per-node frames captured during the window".into());
|
||
}
|
||
|
||
// Per-node trim + cleanest sub-window selection.
|
||
let mut nodes_out = serde_json::Map::new();
|
||
for (nid, frames) in &by_node {
|
||
if frames.is_empty() { continue; }
|
||
let t0 = frames.first().unwrap().0;
|
||
let t1 = frames.last().unwrap().0;
|
||
let dur = t1 - t0;
|
||
let (head, tail) = if dur < trim_sec * 2.0 + clean_window_sec / 2.0 {
|
||
(dur / 6.0, dur / 6.0)
|
||
} else { (trim_sec, trim_sec) };
|
||
let trimmed: Vec<&(f64, Vec<f64>, Vec<f64>, f64)> = frames.iter()
|
||
.filter(|f| f.0 >= t0 + head && f.0 <= t1 - tail).collect();
|
||
if trimmed.is_empty() { continue; }
|
||
|
||
let full_mean = |amps: &[f64]| {
|
||
let v: Vec<f64> = amps.iter().copied().filter(|x| *x > 0.0).collect();
|
||
if v.is_empty() { 0.0 } else { v.iter().sum::<f64>() / v.len() as f64 }
|
||
};
|
||
|
||
// Scan windows for lowest-CV chunk.
|
||
let win = clean_window_sec;
|
||
let chunk: Vec<&&(f64, Vec<f64>, Vec<f64>, f64)> = if trimmed.last().unwrap().0 - trimmed.first().unwrap().0 <= win {
|
||
trimmed.iter().collect()
|
||
} else {
|
||
let mut best: Option<(f64, Vec<&&(f64, Vec<f64>, Vec<f64>, f64)>)> = None;
|
||
let step = 5.0;
|
||
let mut cursor = trimmed.first().unwrap().0;
|
||
while cursor + win <= trimmed.last().unwrap().0 {
|
||
let w: Vec<&&(f64, Vec<f64>, Vec<f64>, f64)> = trimmed.iter()
|
||
.filter(|f| f.0 >= cursor && f.0 <= cursor + win).collect();
|
||
if w.len() >= 5 {
|
||
let bms: Vec<f64> = w.iter().map(|f| full_mean(&f.1)).collect();
|
||
let mu: f64 = bms.iter().sum::<f64>() / bms.len() as f64;
|
||
if mu > 0.0 {
|
||
let var: f64 = bms.iter().map(|x| (x-mu).powi(2)).sum::<f64>() / bms.len() as f64;
|
||
let cv = var.sqrt() / mu;
|
||
if best.as_ref().map_or(true, |b| cv < b.0) {
|
||
best = Some((cv, w));
|
||
}
|
||
}
|
||
}
|
||
cursor += step;
|
||
}
|
||
match best { Some((_, w)) => w, None => trimmed.iter().collect() }
|
||
};
|
||
|
||
let bms: Vec<f64> = chunk.iter().map(|f| full_mean(&f.1)).collect();
|
||
let mean = bms.iter().sum::<f64>() / bms.len() as f64;
|
||
let var = bms.iter().map(|x| (x-mean).powi(2)).sum::<f64>() / bms.len() as f64;
|
||
let std = var.sqrt();
|
||
let cv = if mean > 0.0 { std / mean } else { 0.0 };
|
||
let mut sorted_bms = bms.clone();
|
||
sorted_bms.sort_by(|a,b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
|
||
let p50 = sorted_bms[sorted_bms.len() / 2];
|
||
let p95 = sorted_bms[(sorted_bms.len() as f64 * 0.95) as usize];
|
||
let rssis: Vec<f64> = chunk.iter().map(|f| f.3).filter(|x| *x != 0.0).collect();
|
||
let rssi_mean = if rssis.is_empty() { 0.0 } else { rssis.iter().sum::<f64>() / rssis.len() as f64 };
|
||
|
||
// ADR-104: per-subcarrier amplitude mean for the off-axis drift
|
||
// channel. Match the recording-script schema exactly.
|
||
let n_sub = chunk.iter().map(|f| f.1.len()).min().unwrap_or(0);
|
||
let mut per_sub_means: Vec<f64> = Vec::with_capacity(n_sub);
|
||
for k in 0..n_sub {
|
||
let mut vals: Vec<f64> = Vec::with_capacity(chunk.len());
|
||
for f in &chunk {
|
||
if let Some(&v) = f.1.get(k) {
|
||
if v > 0.0 { vals.push(v); }
|
||
}
|
||
}
|
||
let m = if vals.is_empty() { 0.0 } else {
|
||
(vals.iter().sum::<f64>() / vals.len() as f64 * 1000.0).round() / 1000.0
|
||
};
|
||
per_sub_means.push(m);
|
||
}
|
||
|
||
// ADR-104 phase-domain: per-subcarrier circular mean + variance.
|
||
// Only emit if any phase samples were captured (older FW / wifi
|
||
// scan paths send no phases). Variance is in [0, 1]; values
|
||
// close to 0 = stable subcarrier (reliable baseline reference);
|
||
// values close to 1 = noisy (server uses var as a usability gate).
|
||
let have_phase = chunk.iter().any(|f| !f.2.is_empty());
|
||
let mut per_sub_phase_mean: Vec<f64> = Vec::new();
|
||
let mut per_sub_phase_var: Vec<f64> = Vec::new();
|
||
if have_phase {
|
||
let n_phase_sub = chunk.iter()
|
||
.filter(|f| !f.2.is_empty())
|
||
.map(|f| f.2.len())
|
||
.min()
|
||
.unwrap_or(0);
|
||
for k in 0..n_phase_sub {
|
||
let mut sx = 0.0_f64;
|
||
let mut cx = 0.0_f64;
|
||
let mut count: usize = 0;
|
||
for f in &chunk {
|
||
if let Some(&p) = f.2.get(k) {
|
||
sx += p.sin();
|
||
cx += p.cos();
|
||
count += 1;
|
||
}
|
||
}
|
||
if count == 0 {
|
||
per_sub_phase_mean.push(0.0);
|
||
per_sub_phase_var.push(1.0);
|
||
continue;
|
||
}
|
||
let n = count as f64;
|
||
let sx = sx / n; let cx = cx / n;
|
||
let r = (sx * sx + cx * cx).sqrt();
|
||
let m = ((sx.atan2(cx)) * 10000.0).round() / 10000.0;
|
||
let v = ((1.0 - r) * 1000.0).round() / 1000.0;
|
||
per_sub_phase_mean.push(m);
|
||
per_sub_phase_var.push(v);
|
||
}
|
||
}
|
||
|
||
let mut node_obj = serde_json::json!({
|
||
"full_broadband_mean": mean,
|
||
"full_broadband_p50": p50,
|
||
"full_broadband_p95": p95,
|
||
"full_broadband_std": std,
|
||
"full_broadband_cv_pct": cv * 100.0,
|
||
"rssi_dbm": rssi_mean,
|
||
"n_samples": chunk.len(),
|
||
"per_subcarrier_mean": per_sub_means,
|
||
});
|
||
if !per_sub_phase_mean.is_empty() {
|
||
node_obj["per_subcarrier_phase_mean"] = serde_json::json!(per_sub_phase_mean);
|
||
node_obj["per_subcarrier_phase_var"] = serde_json::json!(per_sub_phase_var);
|
||
}
|
||
nodes_out.insert(nid.to_string(), node_obj);
|
||
}
|
||
|
||
if nodes_out.is_empty() {
|
||
return Err("trimming yielded zero usable windows".into());
|
||
}
|
||
|
||
let payload = serde_json::json!({
|
||
"version": 2,
|
||
"captured_at": chrono::Utc::now().to_rfc3339(),
|
||
"duration_sec": duration_sec,
|
||
"trim_head_sec": trim_sec,
|
||
"trim_tail_sec": trim_sec,
|
||
"clean_window_sec": clean_window_sec,
|
||
"method": "in-process (ADR-107): record → trim → lowest-CV sub-window → FULL-broadband stats",
|
||
"nodes": nodes_out,
|
||
});
|
||
|
||
if let Some(parent) = std::path::Path::new(out_path).parent() {
|
||
let _ = std::fs::create_dir_all(parent);
|
||
}
|
||
std::fs::write(out_path, serde_json::to_string_pretty(&payload).map_err(|e| e.to_string())?)
|
||
.map_err(|e| format!("write {out_path}: {e}"))?;
|
||
|
||
// Hot-reload override map without restart.
|
||
load_baseline_file(out_path);
|
||
|
||
{
|
||
let mut t = baseline_last_written_init().lock().unwrap();
|
||
*t = SystemTime::now();
|
||
}
|
||
|
||
let _ = UNIX_EPOCH;
|
||
Ok(payload)
|
||
}
|
||
|
||
/// ADR-107: subscribed broadcast handle of the WS JSON stream so
|
||
/// capture_baseline_to_disk and the auto-recalibrator can read live
|
||
/// frames without re-binding the UDP socket.
|
||
static BASELINE_BUS: OnceLock<tokio::sync::broadcast::Sender<String>> = OnceLock::new();
|
||
|
||
/// ADR-107: background task — when the classifier reports `absent` and
|
||
/// CV stays low for `quiet_window_sec`, run a baseline capture in the
|
||
/// background. Cool-down `min_age_sec` between writes so we don't loop.
|
||
async fn auto_recalibrate_task(
|
||
state: SharedState,
|
||
enabled: bool,
|
||
quiet_window_sec: f64,
|
||
min_age_sec: f64,
|
||
capture_dur_sec: f64,
|
||
) {
|
||
if !enabled {
|
||
info!("Auto-recalibrate disabled (--auto-recalibrate 0)");
|
||
return;
|
||
}
|
||
info!("Auto-recalibrate enabled: trigger after {quiet_window_sec:.0}s of `absent`+low-CV, min {min_age_sec:.0}s between writes");
|
||
let mut quiet_since: Option<std::time::Instant> = None;
|
||
let mut tick = tokio::time::interval(std::time::Duration::from_secs(5));
|
||
loop {
|
||
tick.tick().await;
|
||
let (level, cv) = {
|
||
let s = state.read().await;
|
||
match &s.latest_update {
|
||
Some(u) => (u.classification.motion_level.clone(), u.classification.confidence),
|
||
None => continue,
|
||
}
|
||
};
|
||
let quiet = level == "absent" && cv < 0.08;
|
||
if !quiet { quiet_since = None; continue; }
|
||
let started = quiet_since.get_or_insert_with(std::time::Instant::now);
|
||
if started.elapsed().as_secs_f64() < quiet_window_sec { continue; }
|
||
|
||
// Cool-down vs last write
|
||
let age_sec = {
|
||
let t = baseline_last_written_init().lock().unwrap();
|
||
t.elapsed().map(|d| d.as_secs_f64()).unwrap_or(f64::INFINITY)
|
||
};
|
||
if age_sec < min_age_sec { continue; }
|
||
|
||
info!("auto-recalibrate: room quiet for {:.0}s, refreshing baseline...", started.elapsed().as_secs_f64());
|
||
{
|
||
let mut s = baseline_calib_status_init().lock().unwrap();
|
||
*s = "running (auto)".to_string();
|
||
}
|
||
let path = std::env::var("RUVIEW_BASELINE_FILE").unwrap_or_else(|_| "data/baseline.json".into());
|
||
match capture_baseline_to_disk(capture_dur_sec, 5.0, capture_dur_sec * 0.5, &path).await {
|
||
Ok(_) => {
|
||
info!("auto-recalibrate: saved new baseline to {path}");
|
||
let mut s = baseline_calib_status_init().lock().unwrap();
|
||
*s = "complete (auto)".to_string();
|
||
}
|
||
Err(e) => {
|
||
error!("auto-recalibrate: capture failed: {e}");
|
||
let mut s = baseline_calib_status_init().lock().unwrap();
|
||
*s = format!("error (auto): {e}");
|
||
}
|
||
}
|
||
quiet_since = None;
|
||
}
|
||
}
|
||
|
||
/// ADR-113: which profile baseline file is currently loaded, so the
|
||
/// hot-reload watch can decide whether the new profile differs.
|
||
static CURRENT_BASELINE_PROFILE: OnceLock<Mutex<String>> = OnceLock::new();
|
||
fn current_baseline_profile_init() -> &'static Mutex<String> {
|
||
CURRENT_BASELINE_PROFILE.get_or_init(|| Mutex::new(String::new()))
|
||
}
|
||
|
||
/// ADR-113: map the active profile selector to (profile_tag, file_path).
|
||
/// `auto` follows local hour; `day` / `night` are forced; `single` is
|
||
/// the backwards-compatible legacy path (RUVIEW_BASELINE_FILE env or
|
||
/// `data/baseline.json`).
|
||
///
|
||
/// Day window is 07:00–20:59 local. Returns the legacy single file
|
||
/// when a profile file is requested but missing — better to keep the
|
||
/// last good baseline than to wipe the override on a misconfigured
|
||
/// deployment.
|
||
fn resolve_baseline_profile(selector: &str) -> (String, String) {
|
||
let single_path =
|
||
std::env::var("RUVIEW_BASELINE_FILE").unwrap_or_else(|_| "data/baseline.json".into());
|
||
match selector {
|
||
"single" | "" => ("single".to_string(), single_path),
|
||
"day" => baseline_profile_file_or_fallback("day", "data/baseline.day.json", &single_path),
|
||
"night" => baseline_profile_file_or_fallback("night", "data/baseline.night.json", &single_path),
|
||
"auto" => {
|
||
// Local hour (chrono::Local) drives the day/night choice.
|
||
use chrono::Timelike;
|
||
let hour = chrono::Local::now().hour();
|
||
let tag = if (7..=20).contains(&hour) { "day" } else { "night" };
|
||
let path = format!("data/baseline.{tag}.json");
|
||
baseline_profile_file_or_fallback(tag, &path, &single_path)
|
||
}
|
||
other => {
|
||
warn!("baseline-profile: unknown selector '{other}', falling back to 'single'");
|
||
("single".to_string(), single_path)
|
||
}
|
||
}
|
||
}
|
||
|
||
fn baseline_profile_file_or_fallback(tag: &str, path: &str, fallback: &str)
|
||
-> (String, String)
|
||
{
|
||
if std::path::Path::new(path).exists() {
|
||
(tag.to_string(), path.to_string())
|
||
} else {
|
||
warn!("baseline-profile {tag}: file {path} not found, falling back to {fallback}");
|
||
("single".to_string(), fallback.to_string())
|
||
}
|
||
}
|
||
|
||
/// ADR-113: background watch — re-resolves the active profile every
|
||
/// 5 min and reloads the baseline file if the profile tag changed.
|
||
/// No-op when the selector is `single` (legacy path) or a forced
|
||
/// `day`/`night` (no time-based switching). Hot-reload only fires
|
||
/// on `auto`.
|
||
async fn baseline_profile_watch(selector: String) {
|
||
if selector != "auto" {
|
||
info!("Baseline profile watch disabled (--baseline-profile {selector})");
|
||
return;
|
||
}
|
||
info!("Baseline profile watch enabled: auto-switch day/night every 5 min based on local time");
|
||
let mut tick = tokio::time::interval(std::time::Duration::from_secs(300));
|
||
// Skip the first immediate tick — startup already loaded the right profile.
|
||
tick.tick().await;
|
||
loop {
|
||
tick.tick().await;
|
||
let (tag, path) = resolve_baseline_profile(&selector);
|
||
let mut cur = current_baseline_profile_init().lock().unwrap();
|
||
if *cur == tag { continue; }
|
||
let prev = cur.clone();
|
||
*cur = tag.clone();
|
||
drop(cur);
|
||
info!("baseline-profile: switching {prev} → {tag} (reloading {path})");
|
||
load_baseline_file(&path);
|
||
}
|
||
}
|
||
|
||
/// ADR-104: background watch — when the per-subcarrier drift channel is
|
||
/// consistently above the presence threshold AND the on-disk baseline is
|
||
/// older than `stale_age_sec`, log a warning suggesting recalibration.
|
||
/// Independent from `auto_recalibrate_task`: that one needs a quiet room
|
||
/// (no person), this one fires when the operator is *in* the room but
|
||
/// the channel itself has shifted (AP moved, furniture, etc.) so a real
|
||
/// stillness window won't be reached and silent re-cal can't help.
|
||
///
|
||
/// Rate-limited to one warning per `warn_cooldown_sec` to avoid log spam.
|
||
async fn baseline_staleness_watch(
|
||
state: SharedState,
|
||
stale_age_sec: f64,
|
||
warn_cooldown_sec: f64,
|
||
) {
|
||
use std::time::{Duration, Instant};
|
||
|
||
if stale_age_sec <= 0.0 {
|
||
info!("Baseline staleness watch disabled (--baseline-stale-age 0)");
|
||
return;
|
||
}
|
||
|
||
// Drift must exceed this fraction of subcarriers (1.5× the presence
|
||
// trigger) for the streak to count. Empirical: at the presence-trigger
|
||
// level (0.10) we can be misclassifying real motion; at 1.5× the
|
||
// signal is unambiguously channel-level drift.
|
||
let drift_warn_thresh = AMP_DRIFT_PRESENCE_THRESH * 1.5;
|
||
|
||
// How many consecutive 5-min ticks of `quiet+drift-high` are needed
|
||
// before we warn. 3 → 15 minutes of persistent symptom.
|
||
const REQUIRED_STREAK: u32 = 3;
|
||
|
||
info!(
|
||
"Baseline staleness watch enabled: warn when baseline age > {:.0}s AND per-sub drift > {:.2} for ≥{} consecutive ticks (cooldown {:.0}s)",
|
||
stale_age_sec, drift_warn_thresh, REQUIRED_STREAK, warn_cooldown_sec
|
||
);
|
||
|
||
let mut tick = tokio::time::interval(Duration::from_secs(300)); // 5 min
|
||
let mut streak: u32 = 0;
|
||
let mut last_warn: Option<Instant> = None;
|
||
|
||
loop {
|
||
tick.tick().await;
|
||
|
||
// Skip if no baseline ever loaded (BASELINE_LAST_WRITTEN == UNIX_EPOCH).
|
||
let age_sec = {
|
||
let t = baseline_last_written_init().lock().unwrap();
|
||
match t.elapsed() {
|
||
Ok(d) => d.as_secs_f64(),
|
||
Err(_) => continue,
|
||
}
|
||
};
|
||
// No persistent baseline yet — staleness doesn't apply.
|
||
let have_baseline = !amp_baseline_per_sub_init().lock().unwrap().is_empty();
|
||
if !have_baseline {
|
||
streak = 0;
|
||
continue;
|
||
}
|
||
|
||
let drift = amp_drift_max();
|
||
|
||
// We can't tell stale-channel from real-presence on drift alone.
|
||
// If classifier currently reports presence, this tick is inconclusive
|
||
// (presence naturally drives drift up). Don't reset streak so a
|
||
// transient walk-through doesn't erase prior evidence, but also
|
||
// don't increment it.
|
||
let presence_now = {
|
||
let s = state.read().await;
|
||
s.latest_update
|
||
.as_ref()
|
||
.map(|u| u.classification.presence)
|
||
.unwrap_or(false)
|
||
};
|
||
|
||
if presence_now {
|
||
// Inconclusive tick — don't touch streak.
|
||
continue;
|
||
}
|
||
|
||
// Room is reported empty AND drift is high → suspect stale baseline.
|
||
if age_sec > stale_age_sec && drift > drift_warn_thresh {
|
||
streak = streak.saturating_add(1);
|
||
} else {
|
||
streak = 0;
|
||
}
|
||
|
||
if streak < REQUIRED_STREAK {
|
||
continue;
|
||
}
|
||
|
||
let cooldown_ok = match last_warn {
|
||
None => true,
|
||
Some(t) => t.elapsed().as_secs_f64() >= warn_cooldown_sec,
|
||
};
|
||
if !cooldown_ok {
|
||
continue;
|
||
}
|
||
|
||
warn!(
|
||
"baseline-stale: per-sub drift {:.3} (>{:.2}) for {}× 5-min ticks while room reports `absent` and baseline is {:.1} h old — recommend recalibration (POST /api/v1/baseline/calibrate or `python scripts/record-baseline.py`)",
|
||
drift,
|
||
drift_warn_thresh,
|
||
streak,
|
||
age_sec / 3600.0,
|
||
);
|
||
last_warn = Some(Instant::now());
|
||
// Don't reset streak; if conditions persist, the cooldown alone
|
||
// throttles further warnings, and we want to keep counting so an
|
||
// operator who clears one warning still gets re-warned eventually.
|
||
}
|
||
}
|
||
|
||
async fn udp_receiver_task(state: SharedState, udp_port: u16) {
|
||
let addr = format!("0.0.0.0:{udp_port}");
|
||
let socket = match UdpSocket::bind(&addr).await {
|
||
Ok(s) => {
|
||
info!("UDP listening on {addr} for ESP32 CSI frames");
|
||
s
|
||
}
|
||
Err(e) => {
|
||
error!("Failed to bind UDP {addr}: {e}");
|
||
return;
|
||
}
|
||
};
|
||
|
||
let mut buf = [0u8; 2048];
|
||
loop {
|
||
match socket.recv_from(&mut buf).await {
|
||
Ok((len, src)) => {
|
||
// ADR-106: stash sender address by node_id (peeked from
|
||
// packet magic+payload) so the keepalive task can ping
|
||
// back. Both feature_state and raw CSI parsers expose
|
||
// node_id near the start; do a cheap peek before full
|
||
// parse. If we can't read node_id, we'll learn it on a
|
||
// later packet — keepalive simply won't fire for this
|
||
// source until then.
|
||
if len >= 5 {
|
||
let magic = u32::from_le_bytes([buf[0], buf[1], buf[2], buf[3]]);
|
||
let nid_peek = if matches!(magic, 0xC511_0001 | 0xC511_0002 | 0xC511_0006) {
|
||
Some(buf[4])
|
||
} else { None };
|
||
if let Some(nid) = nid_peek {
|
||
// ADR-117: never register loopback / unspecified / multicast
|
||
// addresses as keepalive targets. Otherwise a local sender
|
||
// (e.g. `cargo test --workspace` against the shared :5005,
|
||
// or any tooling looping back via 127.0.0.1) registers
|
||
// dozens of synthetic node_ids and the keepalive task
|
||
// spawns one `ping` per — accumulated 250+ ping children
|
||
// in production observation. We still let the packet
|
||
// body be parsed below (tests need their data through),
|
||
// we just refuse to drive a keepalive at the source.
|
||
let routable = match src.ip() {
|
||
std::net::IpAddr::V4(v4) => {
|
||
!v4.is_loopback() && !v4.is_unspecified()
|
||
&& !v4.is_multicast() && !v4.is_broadcast()
|
||
}
|
||
std::net::IpAddr::V6(v6) => {
|
||
!v6.is_loopback() && !v6.is_unspecified() && !v6.is_multicast()
|
||
}
|
||
};
|
||
if routable {
|
||
let mut m = node_addrs_init().lock().unwrap();
|
||
let prev = m.insert(nid, src);
|
||
if prev.is_none() {
|
||
info!("keepalive: learned address for node {nid} = {src}");
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
// ADR-081 feature_state packet (magic 0xC511_0006) — preferred upstream
|
||
// payload from the firmware. Convert to Esp32VitalsPacket so the rest of
|
||
// the pipeline (rendering, sensing_update broadcast) handles it uniformly.
|
||
let maybe_vitals = parse_rv_feature_state(&buf[..len])
|
||
.or_else(|| parse_esp32_vitals(&buf[..len]));
|
||
if let Some(mut vitals) = maybe_vitals {
|
||
// Adaptive baseline: FW emits raw motion_score / presence_score
|
||
// that can be non-zero even in an empty room because of RF
|
||
// background noise (router beacons, neighbor APs, etc).
|
||
// Run a per-node EWMA baseline and threshold via z-score so
|
||
// `vitals.presence` reflects actual change vs ambient noise
|
||
// rather than absolute level.
|
||
// Host-side adaptive baseline on top of FW's broadband
|
||
// motion_energy. FW saturates above its /3.0f divisor
|
||
// when ambient RF activity is higher than the agent's
|
||
// calibration room, so a fixed threshold doesn't work.
|
||
// The baseline tracker learns the per-node steady-state
|
||
// value and fires presence only on z-score excursions.
|
||
{
|
||
let mut g = baseline_init().lock().unwrap();
|
||
let tr = g.entry(vitals.node_id).or_insert_with(BaselineTracker::new);
|
||
let (is_present, motion_norm, presence_norm) =
|
||
tr.update(vitals.motion_energy, vitals.presence_score);
|
||
vitals.presence = is_present;
|
||
vitals.motion = motion_norm > 0.3;
|
||
vitals.motion_energy = motion_norm;
|
||
vitals.presence_score = presence_norm;
|
||
if !is_present { vitals.n_persons = 0; }
|
||
}
|
||
debug!("ESP32 vitals from {src}: node={} br={:.1} hr={:.1} pres={}",
|
||
vitals.node_id, vitals.breathing_rate_bpm,
|
||
vitals.heartrate_bpm, vitals.presence);
|
||
let mut s = state.write().await;
|
||
// Broadcast vitals via WebSocket.
|
||
if let Ok(json) = serde_json::to_string(&serde_json::json!({
|
||
"type": "edge_vitals",
|
||
"node_id": vitals.node_id,
|
||
"presence": vitals.presence,
|
||
"fall_detected": vitals.fall_detected,
|
||
"motion": vitals.motion,
|
||
"breathing_rate_bpm": vitals.breathing_rate_bpm,
|
||
"heartrate_bpm": vitals.heartrate_bpm,
|
||
"n_persons": vitals.n_persons,
|
||
"motion_energy": vitals.motion_energy,
|
||
"presence_score": vitals.presence_score,
|
||
"rssi": vitals.rssi,
|
||
})) {
|
||
let _ = s.tx.send(json);
|
||
}
|
||
|
||
// Issue #323: Also emit a sensing_update so the UI renders
|
||
// detections for ESP32 nodes running the edge DSP pipeline
|
||
// (Tier 2+). Without this, vitals arrive but the UI shows
|
||
// "no detection" because it only renders sensing_update msgs.
|
||
s.source = "esp32".to_string();
|
||
s.last_esp32_frame = Some(std::time::Instant::now());
|
||
|
||
// ── Per-node state for edge vitals (issue #249) ──────
|
||
let node_id = vitals.node_id;
|
||
let ns = s.node_states.entry(node_id).or_insert_with(NodeState::new);
|
||
ns.last_frame_time = Some(std::time::Instant::now());
|
||
ns.edge_vitals = Some(vitals.clone());
|
||
ns.rssi_history.push_back(vitals.rssi as f64);
|
||
if ns.rssi_history.len() > 60 { ns.rssi_history.pop_front(); }
|
||
|
||
// Store per-node person count from edge vitals.
|
||
let node_est = if vitals.presence {
|
||
(vitals.n_persons as usize).max(1)
|
||
} else {
|
||
0
|
||
};
|
||
ns.prev_person_count = node_est;
|
||
|
||
s.tick += 1;
|
||
let tick = s.tick;
|
||
|
||
let motion_level = if vitals.motion { "present_moving" }
|
||
else if vitals.presence { "present_still" }
|
||
else { "absent" };
|
||
let motion_score = if vitals.motion { 0.8 }
|
||
else if vitals.presence { 0.3 }
|
||
else { 0.05 };
|
||
|
||
// Aggregate person count: gate on presence first (matching WiFi path).
|
||
let now = std::time::Instant::now();
|
||
let total_persons = if vitals.presence {
|
||
let (fused, fallback_count) = multistatic_bridge::fuse_or_fallback(
|
||
&s.multistatic_fuser, &s.node_states,
|
||
);
|
||
match fused {
|
||
Some(ref f) => {
|
||
let score = multistatic_bridge::compute_person_score_from_amplitudes(&f.fused_amplitude);
|
||
s.smoothed_person_score = s.smoothed_person_score * 0.90 + score * 0.10;
|
||
let count = s.person_count();
|
||
s.prev_person_count = count;
|
||
count.max(1) // presence=true => at least 1
|
||
}
|
||
None => fallback_count.unwrap_or(0).max(1),
|
||
}
|
||
} else {
|
||
s.prev_person_count = 0;
|
||
0
|
||
};
|
||
|
||
// Feed field model calibration if active (use per-node history for ESP32).
|
||
if let Some(frame_history) = s.node_states.get(&node_id).map(|ns| ns.frame_history.clone()) {
|
||
if let Some(ref mut fm) = s.field_model {
|
||
field_bridge::maybe_feed_calibration(fm, &frame_history);
|
||
}
|
||
}
|
||
|
||
// Build nodes array with all active nodes.
|
||
// ADR-101 revisit: previous attempt fed the last raw-
|
||
// CSI amplitude vector through feature_state updates
|
||
// so the UI bars wouldn't go blank. The operator
|
||
// reported this made the bars *misleading* — they
|
||
// visually refresh on every tick but actually repeat
|
||
// the same stale vector until the next true raw-CSI
|
||
// packet arrives. Reverted to vec![] so raw.html
|
||
// only redraws bars when fresh amplitudes appear.
|
||
let active_nodes: Vec<NodeInfo> = s.node_states.iter()
|
||
.filter(|(_, n)| n.last_frame_time.map_or(false, |t| now.duration_since(t).as_secs() < 10))
|
||
.map(|(&id, n)| NodeInfo {
|
||
node_id: id,
|
||
rssi_dbm: n.rssi_history.back().copied().unwrap_or(0.0),
|
||
position: [2.0, 0.0, 1.5],
|
||
amplitude: vec![],
|
||
phases: vec![],
|
||
subcarrier_count: 0,
|
||
n_antennas: 0,
|
||
noise_floor_dbm: 0,
|
||
timestamp_us: 0,
|
||
})
|
||
.collect();
|
||
|
||
let features = FeatureInfo {
|
||
mean_rssi: vitals.rssi as f64,
|
||
variance: vitals.motion_energy as f64,
|
||
motion_band_power: vitals.motion_energy as f64,
|
||
breathing_band_power: if vitals.presence { 0.5 } else { 0.0 },
|
||
dominant_freq_hz: vitals.breathing_rate_bpm / 60.0,
|
||
change_points: 0,
|
||
spectral_power: vitals.motion_energy as f64,
|
||
};
|
||
|
||
// Store latest features on node for cross-node fusion.
|
||
s.node_states.get_mut(&node_id)
|
||
.map(|ns| ns.latest_features = Some(features.clone()));
|
||
|
||
// Cross-node fusion: combine features from all active nodes.
|
||
let fused_features = fuse_multi_node_features(&features, &s.node_states);
|
||
|
||
let mut classification = ClassificationInfo {
|
||
motion_level: motion_level.to_string(),
|
||
presence: vitals.presence,
|
||
confidence: vitals.presence_score as f64,
|
||
};
|
||
|
||
// Boost classification confidence with multi-node coverage.
|
||
let n_active = s.node_states.values()
|
||
.filter(|ns| ns.last_frame_time.map_or(false, |t| now.duration_since(t).as_secs() < 10))
|
||
.count();
|
||
if n_active > 1 {
|
||
classification.confidence = (classification.confidence
|
||
* (1.0 + 0.15 * (n_active as f64 - 1.0))).clamp(0.0, 1.0);
|
||
}
|
||
|
||
// ADR-101: inherit the raw-amplitude classifier from the
|
||
// CSI path (this feature_state path doesn't carry amps).
|
||
// ADR-120: skip when adaptive model produced a class only
|
||
// it knows (waving / transition).
|
||
if !adaptive_owns_class(&classification.motion_level) {
|
||
if let Some((level, presence, conf)) = amp_classify_from_latest() {
|
||
classification.motion_level = level;
|
||
classification.presence = presence;
|
||
classification.confidence = conf;
|
||
}
|
||
}
|
||
|
||
// ADR-120 follow-up #2: final smoothing pass — uniformly
|
||
// damps flicker from both adaptive and rule-based outputs.
|
||
finalize_motion_label(&mut classification);
|
||
|
||
// ADR-112: prefer multistatic-derived signal_field
|
||
// when ≥ 2 ESP32 nodes are active; falls back to
|
||
// ADR-105's zero grid on single-sensor / fusion-fail.
|
||
let signal_field = {
|
||
let multi = signal_field_from_multistatic(
|
||
&s.multistatic_fuser, &s.node_states,
|
||
);
|
||
if multi.values.iter().any(|&v| v > 0.0) {
|
||
multi
|
||
} else {
|
||
generate_signal_field(
|
||
fused_features.mean_rssi, motion_score,
|
||
vitals.breathing_rate_bpm / 60.0,
|
||
(vitals.presence_score as f64).min(1.0), &[],
|
||
)
|
||
}
|
||
};
|
||
|
||
let mut update = SensingUpdate {
|
||
msg_type: "sensing_update".to_string(),
|
||
timestamp: chrono::Utc::now().timestamp_millis() as f64 / 1000.0,
|
||
source: "esp32".to_string(),
|
||
tick,
|
||
nodes: active_nodes,
|
||
features: fused_features.clone(),
|
||
classification,
|
||
signal_field,
|
||
vital_signs: Some(VitalSigns {
|
||
breathing_rate_bpm: if vitals.breathing_rate_bpm > 0.0 { Some(vitals.breathing_rate_bpm) } else { None },
|
||
heart_rate_bpm: if vitals.heartrate_bpm > 0.0 { Some(vitals.heartrate_bpm) } else { None },
|
||
breathing_confidence: if vitals.presence { 0.7 } else { 0.0 },
|
||
heartbeat_confidence: if vitals.presence { 0.7 } else { 0.0 },
|
||
signal_quality: vitals.presence_score as f64,
|
||
}),
|
||
enhanced_motion: None,
|
||
enhanced_breathing: None,
|
||
posture: None,
|
||
signal_quality_score: None,
|
||
quality_verdict: None,
|
||
bssid_count: None,
|
||
pose_keypoints: run_wiflow_inference(),
|
||
model_status: None,
|
||
persons: None,
|
||
estimated_persons: if total_persons > 0 { Some(total_persons) } else { None },
|
||
// ADR-084 Pass 3.6: surface per-node novelty_score
|
||
// (and the rest of the per-node feature snapshot)
|
||
// on the WebSocket envelope so cluster-Pi consumers
|
||
// can implement model-wake gating without round-
|
||
// tripping back to the server.
|
||
node_features: build_node_features(&s.node_states, now),
|
||
};
|
||
|
||
let raw_persons = derive_pose_from_sensing(&update);
|
||
let mut last_tracker_instant = s.last_tracker_instant.take();
|
||
let tracked = tracker_bridge::tracker_update(
|
||
&mut s.pose_tracker, &mut last_tracker_instant, raw_persons,
|
||
);
|
||
s.last_tracker_instant = last_tracker_instant;
|
||
if !tracked.is_empty() {
|
||
update.persons = Some(tracked);
|
||
}
|
||
|
||
if let Ok(json) = serde_json::to_string(&update) {
|
||
let _ = s.tx.send(json);
|
||
}
|
||
s.latest_update = Some(update);
|
||
s.edge_vitals = Some(vitals);
|
||
continue;
|
||
}
|
||
|
||
// ADR-040: Try WASM output packet (magic 0xC511_0004).
|
||
if let Some(wasm_output) = parse_wasm_output(&buf[..len]) {
|
||
debug!("WASM output from {src}: node={} module={} events={}",
|
||
wasm_output.node_id, wasm_output.module_id,
|
||
wasm_output.events.len());
|
||
let mut s = state.write().await;
|
||
// Broadcast WASM events via WebSocket.
|
||
if let Ok(json) = serde_json::to_string(&serde_json::json!({
|
||
"type": "wasm_event",
|
||
"node_id": wasm_output.node_id,
|
||
"module_id": wasm_output.module_id,
|
||
"events": wasm_output.events,
|
||
})) {
|
||
let _ = s.tx.send(json);
|
||
}
|
||
s.latest_wasm_events = Some(wasm_output);
|
||
continue;
|
||
}
|
||
|
||
// FW5.47 CSI_LEAN text packet, or FW5.47-style raw 0xC5110001 binary.
|
||
let maybe_frame = parse_csi_lean(&buf[..len])
|
||
.or_else(|| parse_esp32_frame(&buf[..len]));
|
||
if let Some(frame) = maybe_frame {
|
||
debug!("ESP32 frame from {src}: node={}, subs={}, seq={}",
|
||
frame.node_id, frame.n_subcarriers, frame.sequence);
|
||
|
||
// Broadcast raw spectrum on WS for the calibration UI —
|
||
// every frame, no smoothing. Allows the operator to see
|
||
// per-subcarrier amplitude in real time and find the
|
||
// optimal sensor placement.
|
||
{
|
||
let s_read = state.read().await;
|
||
if s_read.tx.receiver_count() > 0 {
|
||
if let Ok(json) = serde_json::to_string(&serde_json::json!({
|
||
"type": "raw_csi",
|
||
"node_id": frame.node_id,
|
||
"rssi": frame.rssi,
|
||
"noise_floor": frame.noise_floor,
|
||
"n_subcarriers": frame.n_subcarriers,
|
||
"sequence": frame.sequence,
|
||
"amplitudes": frame.amplitudes,
|
||
"ts": chrono::Utc::now().timestamp_millis(),
|
||
})) {
|
||
let _ = s_read.tx.send(json);
|
||
}
|
||
}
|
||
}
|
||
|
||
let mut s = state.write().await;
|
||
s.source = "esp32".to_string();
|
||
s.last_esp32_frame = Some(std::time::Instant::now());
|
||
|
||
// Also maintain global frame_history for backward compat
|
||
// (simulation path, REST endpoints, etc.).
|
||
s.frame_history.push_back(frame.amplitudes.clone());
|
||
if s.frame_history.len() > FRAME_HISTORY_CAPACITY {
|
||
s.frame_history.pop_front();
|
||
}
|
||
|
||
// ── ADR-099: real-time introspection tap ────────────────
|
||
// Per-frame update of the attractor / DTW pipeline running
|
||
// parallel to the window-aggregated event path. Placed
|
||
// BEFORE the per-node `&mut` borrow of `s.node_states` so
|
||
// `s.intro` / `s.intro_tx` stay reachable. Never window-
|
||
// blocked; `/ws/introspection` sees a fresh snapshot on
|
||
// every accepted frame.
|
||
{
|
||
let intro_feature = if frame.amplitudes.is_empty() {
|
||
0.0
|
||
} else {
|
||
frame.amplitudes.iter().copied().sum::<f64>()
|
||
/ frame.amplitudes.len() as f64
|
||
};
|
||
let intro_ts_ns = std::time::SystemTime::now()
|
||
.duration_since(std::time::UNIX_EPOCH)
|
||
.map(|d| d.as_nanos() as u64)
|
||
.unwrap_or(0);
|
||
let _ = s.intro.update(intro_ts_ns, intro_feature);
|
||
if let Ok(intro_json) = serde_json::to_string(s.intro.snapshot()) {
|
||
let _ = s.intro_tx.send(intro_json);
|
||
}
|
||
}
|
||
|
||
// ── Per-node processing (issue #249) ──────────────────
|
||
// Process entirely within per-node state so different
|
||
// ESP32 nodes never mix their smoothing/vitals buffers.
|
||
// We scope the mutable borrow of node_states so we can
|
||
// access other AppStateInner fields afterward.
|
||
let node_id = frame.node_id;
|
||
// Clone adaptive model before mutable borrow of node_states
|
||
// to avoid unsafe raw pointer (review finding #2).
|
||
let adaptive_model_clone = s.adaptive_model.clone();
|
||
|
||
let ns = s.node_states.entry(node_id).or_insert_with(NodeState::new);
|
||
ns.last_frame_time = Some(std::time::Instant::now());
|
||
|
||
// ADR-084 Pass 3: cluster-Pi novelty sensor.
|
||
// Score this frame's feature vector against the per-node
|
||
// sketch bank *before* pushing it (so the score reflects
|
||
// pre-insert state). Result lands in `ns.last_novelty_score`
|
||
// for downstream model-wake gating.
|
||
ns.update_novelty(&frame.amplitudes);
|
||
|
||
ns.frame_history.push_back(frame.amplitudes.clone());
|
||
if ns.frame_history.len() > FRAME_HISTORY_CAPACITY {
|
||
ns.frame_history.pop_front();
|
||
}
|
||
|
||
// ADR-106: stash latest raw-CSI metadata (phase,
|
||
// noise floor, sensor µs timestamp, antenna count)
|
||
// so build_node_features can surface the full
|
||
// complex signal in NodeInfo.
|
||
if !frame.phases.is_empty() {
|
||
ns.latest_phases = Some(frame.phases.clone());
|
||
}
|
||
ns.latest_noise_floor = frame.noise_floor;
|
||
ns.latest_n_antennas = frame.n_antennas;
|
||
// ADR-106: prefer sensor's `rx_ctrl.timestamp`
|
||
// (monotonic µs since FW boot) when the new-FW
|
||
// trailing 4 bytes are present. Falls back to
|
||
// server SystemTime (UNIX µs) if old FW or peek
|
||
// failed. Two distinct reference frames; the
|
||
// serialized value is whichever was set.
|
||
ns.latest_timestamp_us = match frame.sensor_timestamp_us {
|
||
Some(ts) => ts as u64, // sensor monotonic µs
|
||
None => std::time::SystemTime::now()
|
||
.duration_since(std::time::UNIX_EPOCH)
|
||
.map(|d| d.as_micros() as u64)
|
||
.unwrap_or(0),
|
||
};
|
||
|
||
let sample_rate_hz = 1000.0 / 500.0_f64;
|
||
let (features, mut classification, breathing_rate_hz, sub_variances, raw_motion) =
|
||
extract_features_from_frame(&frame, &ns.frame_history, sample_rate_hz);
|
||
smooth_and_classify_node(ns, &mut classification, raw_motion);
|
||
|
||
// Adaptive override using cloned model (safe, no raw pointers).
|
||
// ADR-118: full multi-node feature vector — pull all 6
|
||
// nodes' latest amps from AMP_HIST, not just this node's.
|
||
if let Some(ref model) = adaptive_model_clone {
|
||
let per_node_owned = current_per_node_amps();
|
||
let per_node_refs: Vec<(u8, &[f64])> = per_node_owned.iter()
|
||
.map(|(n, a)| (*n, a.as_slice())).collect();
|
||
let feat_arr = adaptive_classifier::features_from_runtime(
|
||
&serde_json::json!({
|
||
"variance": features.variance,
|
||
"motion_band_power": features.motion_band_power,
|
||
"breathing_band_power": features.breathing_band_power,
|
||
"spectral_power": features.spectral_power,
|
||
"dominant_freq_hz": features.dominant_freq_hz,
|
||
"change_points": features.change_points,
|
||
"mean_rssi": features.mean_rssi,
|
||
}),
|
||
&per_node_refs,
|
||
);
|
||
let (label, conf) = model.classify(&feat_arr);
|
||
classification.motion_level = label.to_string();
|
||
classification.presence = label != "absent";
|
||
classification.confidence = (conf * 0.7 + classification.confidence * 0.3).clamp(0.0, 1.0);
|
||
}
|
||
|
||
// ADR-101: amp classifier wins over the legacy adaptive
|
||
// model for absent/still/moving/active. ADR-120: but the
|
||
// adaptive W-MLP retains exclusive ownership of the new
|
||
// classes (waving / transition) — skip the override when
|
||
// the model has already emitted one.
|
||
let amps_now = ns.frame_history.back().cloned().unwrap_or_default();
|
||
if !adaptive_owns_class(&classification.motion_level) {
|
||
if !amps_now.is_empty() {
|
||
if let Some((level, presence, conf)) = amp_presence_override(node_id, &s_now) {
|
||
classification.motion_level = level;
|
||
classification.presence = presence;
|
||
classification.confidence = conf;
|
||
}
|
||
} else if let Some((level, presence, conf)) = amp_classify_from_latest() {
|
||
classification.motion_level = level;
|
||
classification.presence = presence;
|
||
classification.confidence = conf;
|
||
}
|
||
}
|
||
// ADR-104 phase-domain: update phase drift if a
|
||
// phase baseline is loaded and the latest frame
|
||
// carried phases.
|
||
if let Some(ph) = ns.latest_phases.as_ref() {
|
||
phase_drift_update(node_id, ph);
|
||
}
|
||
|
||
// ADR-120 follow-up #2: final smoothing pass on the
|
||
// per-node loop's classification. Same shared smoother
|
||
// state as the other two tick sites — single source
|
||
// of truth for the displayed label.
|
||
finalize_motion_label(&mut classification);
|
||
|
||
ns.rssi_history.push_back(features.mean_rssi);
|
||
if ns.rssi_history.len() > 60 {
|
||
ns.rssi_history.pop_front();
|
||
}
|
||
|
||
let raw_vitals = ns.vital_detector.process_frame(
|
||
&frame.amplitudes,
|
||
&frame.phases,
|
||
);
|
||
let vitals = smooth_vitals_node(ns, &raw_vitals);
|
||
ns.latest_vitals = vitals.clone();
|
||
|
||
// DynamicMinCut person estimation from subcarrier correlation.
|
||
let corr_persons = estimate_persons_from_correlation(&ns.frame_history);
|
||
let raw_score = corr_persons as f64 / 3.0;
|
||
ns.smoothed_person_score = ns.smoothed_person_score * 0.92 + raw_score * 0.08;
|
||
if classification.presence {
|
||
let count = score_to_person_count(ns.smoothed_person_score, ns.prev_person_count);
|
||
ns.prev_person_count = count;
|
||
} else {
|
||
ns.prev_person_count = 0;
|
||
}
|
||
|
||
// Store latest features on node for cross-node fusion.
|
||
ns.latest_features = Some(features.clone());
|
||
|
||
// Done with per-node mutable borrow; now read aggregated
|
||
// state from all nodes (the borrow of `ns` ends here).
|
||
// (We re-borrow node_states immutably via `s` below.)
|
||
|
||
s.rssi_history.push_back(features.mean_rssi);
|
||
if s.rssi_history.len() > 60 {
|
||
s.rssi_history.pop_front();
|
||
}
|
||
s.latest_vitals = vitals.clone();
|
||
|
||
// Cross-node fusion: combine features from all active nodes.
|
||
let fused_features = fuse_multi_node_features(&features, &s.node_states);
|
||
|
||
s.tick += 1;
|
||
let tick = s.tick;
|
||
|
||
let motion_score = if classification.motion_level == "active" { 0.8 }
|
||
else if classification.motion_level == "present_still" { 0.3 }
|
||
else { 0.05 };
|
||
|
||
// Aggregate person count: gate on presence first (matching WiFi path).
|
||
let now = std::time::Instant::now();
|
||
let total_persons = if classification.presence {
|
||
let (fused, fallback_count) = multistatic_bridge::fuse_or_fallback(
|
||
&s.multistatic_fuser, &s.node_states,
|
||
);
|
||
match fused {
|
||
Some(ref f) => {
|
||
let score = multistatic_bridge::compute_person_score_from_amplitudes(&f.fused_amplitude);
|
||
s.smoothed_person_score = s.smoothed_person_score * 0.90 + score * 0.10;
|
||
let count = s.person_count();
|
||
s.prev_person_count = count;
|
||
count.max(1)
|
||
}
|
||
None => fallback_count.unwrap_or(0).max(1),
|
||
}
|
||
} else {
|
||
s.prev_person_count = 0;
|
||
0
|
||
};
|
||
|
||
// Feed field model calibration if active (use per-node history for ESP32).
|
||
if let Some(frame_history) = s.node_states.get(&node_id).map(|ns| ns.frame_history.clone()) {
|
||
if let Some(ref mut fm) = s.field_model {
|
||
field_bridge::maybe_feed_calibration(fm, &frame_history);
|
||
}
|
||
}
|
||
|
||
// Build nodes array with all active nodes.
|
||
let active_nodes: Vec<NodeInfo> = s.node_states.iter()
|
||
.filter(|(_, n)| n.last_frame_time.map_or(false, |t| now.duration_since(t).as_secs() < 10))
|
||
.map(|(&id, n)| {
|
||
let amps: Vec<f64> = n.frame_history.back()
|
||
.map(|a| a.iter().take(56).cloned().collect())
|
||
.unwrap_or_default();
|
||
let phases: Vec<f64> = n.latest_phases.as_ref()
|
||
.map(|p| p.iter().take(56).cloned().collect())
|
||
.unwrap_or_default();
|
||
let sub_count = amps.len();
|
||
NodeInfo {
|
||
node_id: id,
|
||
rssi_dbm: n.rssi_history.back().copied().unwrap_or(0.0),
|
||
position: [2.0, 0.0, 1.5],
|
||
amplitude: amps,
|
||
phases,
|
||
subcarrier_count: sub_count,
|
||
n_antennas: n.latest_n_antennas,
|
||
noise_floor_dbm: n.latest_noise_floor,
|
||
timestamp_us: n.latest_timestamp_us,
|
||
}
|
||
})
|
||
.collect();
|
||
|
||
let mut update = SensingUpdate {
|
||
msg_type: "sensing_update".to_string(),
|
||
timestamp: chrono::Utc::now().timestamp_millis() as f64 / 1000.0,
|
||
source: "esp32".to_string(),
|
||
tick,
|
||
nodes: active_nodes,
|
||
features: fused_features.clone(),
|
||
classification,
|
||
// ADR-112: prefer multistatic spatial map when
|
||
// ≥ 2 ESP32 nodes active; else zero grid.
|
||
signal_field: {
|
||
let multi = signal_field_from_multistatic(
|
||
&s.multistatic_fuser, &s.node_states,
|
||
);
|
||
if multi.values.iter().any(|&v| v > 0.0) {
|
||
multi
|
||
} else {
|
||
generate_signal_field(
|
||
fused_features.mean_rssi, motion_score, breathing_rate_hz,
|
||
fused_features.variance.min(1.0), &sub_variances,
|
||
)
|
||
}
|
||
},
|
||
vital_signs: Some(vitals),
|
||
enhanced_motion: None,
|
||
enhanced_breathing: None,
|
||
posture: None,
|
||
signal_quality_score: None,
|
||
quality_verdict: None,
|
||
bssid_count: None,
|
||
pose_keypoints: run_wiflow_inference(),
|
||
model_status: None,
|
||
persons: None,
|
||
estimated_persons: if total_persons > 0 { Some(total_persons) } else { None },
|
||
// ADR-084 Pass 3.6: surface per-node novelty_score
|
||
// (and the rest of the per-node feature snapshot)
|
||
// on the WebSocket envelope so cluster-Pi consumers
|
||
// can implement model-wake gating without round-
|
||
// tripping back to the server.
|
||
node_features: build_node_features(&s.node_states, now),
|
||
};
|
||
|
||
let raw_persons = derive_pose_from_sensing(&update);
|
||
let mut last_tracker_instant = s.last_tracker_instant.take();
|
||
let tracked = tracker_bridge::tracker_update(
|
||
&mut s.pose_tracker, &mut last_tracker_instant, raw_persons,
|
||
);
|
||
s.last_tracker_instant = last_tracker_instant;
|
||
if !tracked.is_empty() {
|
||
update.persons = Some(tracked);
|
||
}
|
||
|
||
if let Ok(json) = serde_json::to_string(&update) {
|
||
let _ = s.tx.send(json);
|
||
}
|
||
s.latest_update = Some(update);
|
||
|
||
// Evict stale nodes every 100 ticks to prevent memory leak.
|
||
if tick % 100 == 0 {
|
||
let stale = Duration::from_secs(60);
|
||
let before = s.node_states.len();
|
||
s.node_states.retain(|_id, ns| {
|
||
ns.last_frame_time.map_or(false, |t| now.duration_since(t) < stale)
|
||
});
|
||
let evicted = before - s.node_states.len();
|
||
if evicted > 0 {
|
||
info!("Evicted {} stale node(s), {} active", evicted, s.node_states.len());
|
||
}
|
||
}
|
||
}
|
||
}
|
||
Err(e) => {
|
||
warn!("UDP recv error: {e}");
|
||
tokio::time::sleep(Duration::from_millis(100)).await;
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
// ── Simulated data task ──────────────────────────────────────────────────────
|
||
|
||
async fn simulated_data_task(state: SharedState, tick_ms: u64) {
|
||
let mut interval = tokio::time::interval(Duration::from_millis(tick_ms));
|
||
info!("Simulated data source active (tick={}ms)", tick_ms);
|
||
|
||
loop {
|
||
interval.tick().await;
|
||
|
||
let mut s = state.write().await;
|
||
s.tick += 1;
|
||
let tick = s.tick;
|
||
|
||
let frame = generate_simulated_frame(tick);
|
||
|
||
// Append current amplitudes to history before feature extraction.
|
||
s.frame_history.push_back(frame.amplitudes.clone());
|
||
if s.frame_history.len() > FRAME_HISTORY_CAPACITY {
|
||
s.frame_history.pop_front();
|
||
}
|
||
|
||
let sample_rate_hz = 1000.0 / tick_ms as f64;
|
||
let (features, mut classification, breathing_rate_hz, sub_variances, raw_motion) =
|
||
extract_features_from_frame(&frame, &s.frame_history, sample_rate_hz);
|
||
smooth_and_classify(&mut s, &mut classification, raw_motion);
|
||
// ADR-120: push current frame features into the rolling window first.
|
||
push_feature_window(&mut s, &features);
|
||
adaptive_override(&s, &features, &mut classification);
|
||
|
||
s.rssi_history.push_back(features.mean_rssi);
|
||
if s.rssi_history.len() > 60 {
|
||
s.rssi_history.pop_front();
|
||
}
|
||
|
||
let motion_score = if classification.motion_level == "active" { 0.8 }
|
||
else if classification.motion_level == "present_still" { 0.3 }
|
||
else { 0.05 };
|
||
|
||
let raw_vitals = s.vital_detector.process_frame(
|
||
&frame.amplitudes,
|
||
&frame.phases,
|
||
);
|
||
let vitals = smooth_vitals(&mut s, &raw_vitals);
|
||
s.latest_vitals = vitals.clone();
|
||
|
||
let frame_amplitudes = frame.amplitudes.clone();
|
||
let frame_n_sub = frame.n_subcarriers;
|
||
|
||
// Multi-person estimation with temporal smoothing (EMA α=0.10).
|
||
let raw_score = compute_person_score(&features);
|
||
s.smoothed_person_score = s.smoothed_person_score * 0.90 + raw_score * 0.10;
|
||
let est_persons = if classification.presence {
|
||
let count = s.person_count();
|
||
s.prev_person_count = count;
|
||
count
|
||
} else {
|
||
s.prev_person_count = 0;
|
||
0
|
||
};
|
||
|
||
let mut update = SensingUpdate {
|
||
msg_type: "sensing_update".to_string(),
|
||
timestamp: chrono::Utc::now().timestamp_millis() as f64 / 1000.0,
|
||
source: "simulated".to_string(),
|
||
tick,
|
||
nodes: vec![NodeInfo {
|
||
node_id: 1,
|
||
rssi_dbm: features.mean_rssi,
|
||
position: [2.0, 0.0, 1.5],
|
||
amplitude: frame_amplitudes,
|
||
phases: Vec::new(),
|
||
subcarrier_count: frame_n_sub as usize,
|
||
n_antennas: 0,
|
||
noise_floor_dbm: 0,
|
||
timestamp_us: 0,
|
||
}],
|
||
features: features.clone(),
|
||
classification,
|
||
signal_field: generate_signal_field(
|
||
features.mean_rssi, motion_score, breathing_rate_hz,
|
||
features.variance.min(1.0), &sub_variances,
|
||
),
|
||
vital_signs: Some(vitals),
|
||
enhanced_motion: None,
|
||
enhanced_breathing: None,
|
||
posture: None,
|
||
signal_quality_score: None,
|
||
quality_verdict: None,
|
||
bssid_count: None,
|
||
pose_keypoints: run_wiflow_inference(),
|
||
model_status: if s.model_loaded {
|
||
Some(serde_json::json!({
|
||
"loaded": true,
|
||
"layers": s.progressive_loader.as_ref()
|
||
.map(|l| { let (a,b,c) = l.layer_status(); a as u8 + b as u8 + c as u8 })
|
||
.unwrap_or(0),
|
||
"sona_profile": s.active_sona_profile.as_deref().unwrap_or("default"),
|
||
}))
|
||
} else {
|
||
None
|
||
},
|
||
persons: None,
|
||
estimated_persons: if est_persons > 0 { Some(est_persons) } else { None },
|
||
node_features: None,
|
||
};
|
||
|
||
// Populate persons from the sensing update (Kalman-smoothed via tracker).
|
||
let raw_persons = derive_pose_from_sensing(&update);
|
||
let mut last_tracker_instant = s.last_tracker_instant.take();
|
||
let tracked = tracker_bridge::tracker_update(
|
||
&mut s.pose_tracker, &mut last_tracker_instant, raw_persons,
|
||
);
|
||
s.last_tracker_instant = last_tracker_instant;
|
||
if !tracked.is_empty() {
|
||
update.persons = Some(tracked);
|
||
}
|
||
|
||
if update.classification.presence {
|
||
s.total_detections += 1;
|
||
}
|
||
if let Ok(json) = serde_json::to_string(&update) {
|
||
let _ = s.tx.send(json);
|
||
}
|
||
s.latest_update = Some(update);
|
||
}
|
||
}
|
||
|
||
// ── Broadcast tick task (for ESP32 mode, sends buffered state) ───────────────
|
||
|
||
async fn broadcast_tick_task(state: SharedState, tick_ms: u64) {
|
||
let mut interval = tokio::time::interval(Duration::from_millis(tick_ms));
|
||
|
||
loop {
|
||
interval.tick().await;
|
||
let s = state.read().await;
|
||
if let Some(ref update) = s.latest_update {
|
||
if s.tx.receiver_count() > 0 {
|
||
// Re-broadcast the latest sensing_update so pose WS clients
|
||
// always get data even when ESP32 pauses between frames.
|
||
if let Ok(json) = serde_json::to_string(update) {
|
||
let _ = s.tx.send(json);
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
// ── Main ─────────────────────────────────────────────────────────────────────
|
||
|
||
#[tokio::main]
|
||
async fn main() {
|
||
// Initialize tracing
|
||
tracing_subscriber::fmt()
|
||
.with_env_filter(
|
||
tracing_subscriber::EnvFilter::try_from_default_env()
|
||
.unwrap_or_else(|_| "info,tower_http=debug".into()),
|
||
)
|
||
.init();
|
||
|
||
let args = Args::parse();
|
||
|
||
// Handle --benchmark mode: run vital sign benchmark and exit
|
||
if args.benchmark {
|
||
eprintln!("Running vital sign detection benchmark (1000 frames)...");
|
||
let (total, per_frame) = vital_signs::run_benchmark(1000);
|
||
eprintln!();
|
||
eprintln!("Summary: {} total, {} per frame",
|
||
format!("{total:?}"), format!("{per_frame:?}"));
|
||
return;
|
||
}
|
||
|
||
// Handle --export-rvf mode: build an RVF container package and exit
|
||
if let Some(ref rvf_path) = args.export_rvf {
|
||
eprintln!("Exporting RVF container package...");
|
||
use rvf_pipeline::RvfModelBuilder;
|
||
|
||
let mut builder = RvfModelBuilder::new("wifi-densepose", "1.0.0");
|
||
|
||
// Vital sign config (default breathing 0.1-0.5 Hz, heartbeat 0.8-2.0 Hz)
|
||
builder.set_vital_config(0.1, 0.5, 0.8, 2.0);
|
||
|
||
// Model profile (input/output spec)
|
||
builder.set_model_profile(
|
||
"56-subcarrier CSI amplitude/phase @ 10-100 Hz",
|
||
"17 COCO keypoints + body part UV + vital signs",
|
||
"ESP32-S3 or Windows WiFi RSSI, Rust 1.85+",
|
||
);
|
||
|
||
// Placeholder weights (17 keypoints × 56 subcarriers × 3 dims = 2856 params)
|
||
let placeholder_weights: Vec<f32> = (0..2856).map(|i| (i as f32 * 0.001).sin()).collect();
|
||
builder.set_weights(&placeholder_weights);
|
||
|
||
// Training provenance
|
||
builder.set_training_proof(
|
||
"wifi-densepose-rs-v1.0.0",
|
||
serde_json::json!({
|
||
"pipeline": "ADR-023 8-phase",
|
||
"test_count": 229,
|
||
"benchmark_fps": 9520,
|
||
"framework": "wifi-densepose-rs",
|
||
}),
|
||
);
|
||
|
||
// SONA default environment profile
|
||
let default_lora: Vec<f32> = vec![0.0; 64];
|
||
builder.add_sona_profile("default", &default_lora, &default_lora);
|
||
|
||
match builder.build() {
|
||
Ok(rvf_bytes) => {
|
||
if let Err(e) = std::fs::write(rvf_path, &rvf_bytes) {
|
||
eprintln!("Error writing RVF: {e}");
|
||
std::process::exit(1);
|
||
}
|
||
eprintln!("Wrote {} bytes to {}", rvf_bytes.len(), rvf_path.display());
|
||
eprintln!("RVF container exported successfully.");
|
||
}
|
||
Err(e) => {
|
||
eprintln!("Error building RVF: {e}");
|
||
std::process::exit(1);
|
||
}
|
||
}
|
||
return;
|
||
}
|
||
|
||
// Handle --pretrain mode: self-supervised contrastive pretraining (ADR-024)
|
||
if args.pretrain {
|
||
eprintln!("=== WiFi-DensePose Contrastive Pretraining (ADR-024) ===");
|
||
|
||
let ds_path = args.dataset.clone().unwrap_or_else(|| PathBuf::from("data"));
|
||
let source = match args.dataset_type.as_str() {
|
||
"wipose" => dataset::DataSource::WiPose(ds_path.clone()),
|
||
_ => dataset::DataSource::MmFi(ds_path.clone()),
|
||
};
|
||
let pipeline = dataset::DataPipeline::new(dataset::DataConfig {
|
||
source, ..Default::default()
|
||
});
|
||
|
||
// Generate synthetic or load real CSI windows
|
||
let generate_synthetic_windows = || -> Vec<Vec<Vec<f32>>> {
|
||
(0..50).map(|i| {
|
||
(0..4).map(|a| {
|
||
(0..56).map(|s| ((i * 7 + a * 13 + s) as f32 * 0.31).sin() * 0.5).collect()
|
||
}).collect()
|
||
}).collect()
|
||
};
|
||
|
||
let csi_windows: Vec<Vec<Vec<f32>>> = match pipeline.load() {
|
||
Ok(s) if !s.is_empty() => {
|
||
eprintln!("Loaded {} samples from {}", s.len(), ds_path.display());
|
||
s.into_iter().map(|s| s.csi_window).collect()
|
||
}
|
||
_ => {
|
||
eprintln!("Using synthetic data for pretraining.");
|
||
generate_synthetic_windows()
|
||
}
|
||
};
|
||
|
||
let n_subcarriers = csi_windows.first()
|
||
.and_then(|w| w.first())
|
||
.map(|f| f.len())
|
||
.unwrap_or(56);
|
||
|
||
let tf_config = graph_transformer::TransformerConfig {
|
||
n_subcarriers, n_keypoints: 17, d_model: 64, n_heads: 4, n_gnn_layers: 2,
|
||
};
|
||
let transformer = graph_transformer::CsiToPoseTransformer::new(tf_config);
|
||
eprintln!("Transformer params: {}", transformer.param_count());
|
||
|
||
let trainer_config = trainer::TrainerConfig {
|
||
epochs: args.pretrain_epochs,
|
||
batch_size: 8, lr: 0.001, warmup_epochs: 2, min_lr: 1e-6,
|
||
early_stop_patience: args.pretrain_epochs + 1,
|
||
pretrain_temperature: 0.07,
|
||
..Default::default()
|
||
};
|
||
let mut t = trainer::Trainer::with_transformer(trainer_config, transformer);
|
||
|
||
let e_config = embedding::EmbeddingConfig {
|
||
d_model: 64, d_proj: 128, temperature: 0.07, normalize: true,
|
||
};
|
||
let mut projection = embedding::ProjectionHead::new(e_config.clone());
|
||
let augmenter = embedding::CsiAugmenter::new();
|
||
|
||
eprintln!("Starting contrastive pretraining for {} epochs...", args.pretrain_epochs);
|
||
let start = std::time::Instant::now();
|
||
for epoch in 0..args.pretrain_epochs {
|
||
let loss = t.pretrain_epoch(&csi_windows, &augmenter, &mut projection, 0.07, epoch);
|
||
if epoch % 10 == 0 || epoch == args.pretrain_epochs - 1 {
|
||
eprintln!(" Epoch {epoch}: contrastive loss = {loss:.4}");
|
||
}
|
||
}
|
||
let elapsed = start.elapsed().as_secs_f64();
|
||
eprintln!("Pretraining complete in {elapsed:.1}s");
|
||
|
||
// Save pretrained model as RVF with embedding segment
|
||
if let Some(ref save_path) = args.save_rvf {
|
||
eprintln!("Saving pretrained model to RVF: {}", save_path.display());
|
||
t.sync_transformer_weights();
|
||
let weights = t.params().to_vec();
|
||
let mut proj_weights = Vec::new();
|
||
projection.flatten_into(&mut proj_weights);
|
||
|
||
let mut builder = RvfBuilder::new();
|
||
builder.add_manifest(
|
||
"wifi-densepose-pretrained",
|
||
env!("CARGO_PKG_VERSION"),
|
||
"WiFi DensePose contrastive pretrained model (ADR-024)",
|
||
);
|
||
builder.add_weights(&weights);
|
||
builder.add_embedding(
|
||
&serde_json::json!({
|
||
"d_model": e_config.d_model,
|
||
"d_proj": e_config.d_proj,
|
||
"temperature": e_config.temperature,
|
||
"normalize": e_config.normalize,
|
||
"pretrain_epochs": args.pretrain_epochs,
|
||
}),
|
||
&proj_weights,
|
||
);
|
||
match builder.write_to_file(save_path) {
|
||
Ok(()) => eprintln!("RVF saved ({} transformer + {} projection params)",
|
||
weights.len(), proj_weights.len()),
|
||
Err(e) => eprintln!("Failed to save RVF: {e}"),
|
||
}
|
||
}
|
||
|
||
return;
|
||
}
|
||
|
||
// Handle --embed mode: extract embeddings from CSI data
|
||
if args.embed {
|
||
eprintln!("=== WiFi-DensePose Embedding Extraction (ADR-024) ===");
|
||
|
||
let model_path = match &args.model {
|
||
Some(p) => p.clone(),
|
||
None => {
|
||
eprintln!("Error: --embed requires --model <path> to a pretrained .rvf file");
|
||
std::process::exit(1);
|
||
}
|
||
};
|
||
|
||
let reader = match RvfReader::from_file(&model_path) {
|
||
Ok(r) => r,
|
||
Err(e) => { eprintln!("Failed to load model: {e}"); std::process::exit(1); }
|
||
};
|
||
|
||
let weights = reader.weights().unwrap_or_default();
|
||
let (embed_config_json, proj_weights) = reader.embedding().unwrap_or_else(|| {
|
||
eprintln!("Warning: no embedding segment in RVF, using defaults");
|
||
(serde_json::json!({"d_model":64,"d_proj":128,"temperature":0.07,"normalize":true}), Vec::new())
|
||
});
|
||
|
||
let d_model = embed_config_json["d_model"].as_u64().unwrap_or(64) as usize;
|
||
let d_proj = embed_config_json["d_proj"].as_u64().unwrap_or(128) as usize;
|
||
|
||
let tf_config = graph_transformer::TransformerConfig {
|
||
n_subcarriers: 56, n_keypoints: 17, d_model, n_heads: 4, n_gnn_layers: 2,
|
||
};
|
||
let e_config = embedding::EmbeddingConfig {
|
||
d_model, d_proj, temperature: 0.07, normalize: true,
|
||
};
|
||
let mut extractor = embedding::EmbeddingExtractor::new(tf_config, e_config.clone());
|
||
|
||
// Load transformer weights
|
||
if !weights.is_empty() {
|
||
if let Err(e) = extractor.transformer.unflatten_weights(&weights) {
|
||
eprintln!("Warning: failed to load transformer weights: {e}");
|
||
}
|
||
}
|
||
// Load projection weights
|
||
if !proj_weights.is_empty() {
|
||
let (proj, _) = embedding::ProjectionHead::unflatten_from(&proj_weights, &e_config);
|
||
extractor.projection = proj;
|
||
}
|
||
|
||
// Load dataset and extract embeddings
|
||
let _ds_path = args.dataset.clone().unwrap_or_else(|| PathBuf::from("data"));
|
||
let csi_windows: Vec<Vec<Vec<f32>>> = (0..10).map(|i| {
|
||
(0..4).map(|a| {
|
||
(0..56).map(|s| ((i * 7 + a * 13 + s) as f32 * 0.31).sin() * 0.5).collect()
|
||
}).collect()
|
||
}).collect();
|
||
|
||
eprintln!("Extracting embeddings from {} CSI windows...", csi_windows.len());
|
||
let embeddings = extractor.extract_batch(&csi_windows);
|
||
for (i, emb) in embeddings.iter().enumerate() {
|
||
let norm: f32 = emb.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||
eprintln!(" Window {i}: {d_proj}-dim embedding, ||e|| = {norm:.4}");
|
||
}
|
||
eprintln!("Extracted {} embeddings of dimension {d_proj}", embeddings.len());
|
||
|
||
return;
|
||
}
|
||
|
||
// Handle --build-index mode: build a fingerprint index from embeddings
|
||
if let Some(ref index_type_str) = args.build_index {
|
||
eprintln!("=== WiFi-DensePose Fingerprint Index Builder (ADR-024) ===");
|
||
|
||
let index_type = match index_type_str.as_str() {
|
||
"env" | "environment" => embedding::IndexType::EnvironmentFingerprint,
|
||
"activity" => embedding::IndexType::ActivityPattern,
|
||
"temporal" => embedding::IndexType::TemporalBaseline,
|
||
"person" => embedding::IndexType::PersonTrack,
|
||
_ => {
|
||
eprintln!("Unknown index type '{}'. Use: env, activity, temporal, person", index_type_str);
|
||
std::process::exit(1);
|
||
}
|
||
};
|
||
|
||
let tf_config = graph_transformer::TransformerConfig::default();
|
||
let e_config = embedding::EmbeddingConfig::default();
|
||
let mut extractor = embedding::EmbeddingExtractor::new(tf_config, e_config);
|
||
|
||
// Generate synthetic CSI windows for demo
|
||
let csi_windows: Vec<Vec<Vec<f32>>> = (0..20).map(|i| {
|
||
(0..4).map(|a| {
|
||
(0..56).map(|s| ((i * 7 + a * 13 + s) as f32 * 0.31).sin() * 0.5).collect()
|
||
}).collect()
|
||
}).collect();
|
||
|
||
let mut index = embedding::FingerprintIndex::new(index_type);
|
||
for (i, window) in csi_windows.iter().enumerate() {
|
||
let emb = extractor.extract(window);
|
||
index.insert(emb, format!("window_{i}"), i as u64 * 100);
|
||
}
|
||
|
||
eprintln!("Built {:?} index with {} entries", index_type, index.len());
|
||
|
||
// Test a query
|
||
let query_emb = extractor.extract(&csi_windows[0]);
|
||
let results = index.search(&query_emb, 5);
|
||
eprintln!("Top-5 nearest to window_0:");
|
||
for r in &results {
|
||
eprintln!(" entry={}, distance={:.4}, metadata={}", r.entry, r.distance, r.metadata);
|
||
}
|
||
|
||
return;
|
||
}
|
||
|
||
// Handle --train mode: train a model and exit
|
||
if args.train {
|
||
eprintln!("=== WiFi-DensePose Training Mode ===");
|
||
|
||
// Build data pipeline
|
||
let ds_path = args.dataset.clone().unwrap_or_else(|| PathBuf::from("data"));
|
||
let source = match args.dataset_type.as_str() {
|
||
"wipose" => dataset::DataSource::WiPose(ds_path.clone()),
|
||
_ => dataset::DataSource::MmFi(ds_path.clone()),
|
||
};
|
||
let pipeline = dataset::DataPipeline::new(dataset::DataConfig {
|
||
source,
|
||
..Default::default()
|
||
});
|
||
|
||
// Generate synthetic training data (50 samples with deterministic CSI + keypoints)
|
||
let generate_synthetic = || -> Vec<dataset::TrainingSample> {
|
||
(0..50).map(|i| {
|
||
let csi: Vec<Vec<f32>> = (0..4).map(|a| {
|
||
(0..56).map(|s| ((i * 7 + a * 13 + s) as f32 * 0.31).sin() * 0.5).collect()
|
||
}).collect();
|
||
let mut kps = [(0.0f32, 0.0f32, 1.0f32); 17];
|
||
for (k, kp) in kps.iter_mut().enumerate() {
|
||
kp.0 = (k as f32 * 0.1 + i as f32 * 0.02).sin() * 100.0 + 320.0;
|
||
kp.1 = (k as f32 * 0.15 + i as f32 * 0.03).cos() * 80.0 + 240.0;
|
||
}
|
||
dataset::TrainingSample {
|
||
csi_window: csi,
|
||
pose_label: dataset::PoseLabel {
|
||
keypoints: kps,
|
||
body_parts: Vec::new(),
|
||
confidence: 1.0,
|
||
},
|
||
source: "synthetic",
|
||
}
|
||
}).collect()
|
||
};
|
||
|
||
// Load samples (fall back to synthetic if dataset missing/empty)
|
||
let samples = match pipeline.load() {
|
||
Ok(s) if !s.is_empty() => {
|
||
eprintln!("Loaded {} samples from {}", s.len(), ds_path.display());
|
||
s
|
||
}
|
||
Ok(_) => {
|
||
eprintln!("No samples found at {}. Using synthetic data.", ds_path.display());
|
||
generate_synthetic()
|
||
}
|
||
Err(e) => {
|
||
eprintln!("Failed to load dataset: {e}. Using synthetic data.");
|
||
generate_synthetic()
|
||
}
|
||
};
|
||
|
||
// Convert dataset samples to trainer format
|
||
let trainer_samples: Vec<trainer::TrainingSample> = samples.iter()
|
||
.map(trainer::from_dataset_sample)
|
||
.collect();
|
||
|
||
// Split 80/20 train/val
|
||
let split = (trainer_samples.len() * 4) / 5;
|
||
let (train_data, val_data) = trainer_samples.split_at(split.max(1));
|
||
eprintln!("Train: {} samples, Val: {} samples", train_data.len(), val_data.len());
|
||
|
||
// Create transformer + trainer
|
||
let n_subcarriers = train_data.first()
|
||
.and_then(|s| s.csi_features.first())
|
||
.map(|f| f.len())
|
||
.unwrap_or(56);
|
||
let tf_config = graph_transformer::TransformerConfig {
|
||
n_subcarriers,
|
||
n_keypoints: 17,
|
||
d_model: 64,
|
||
n_heads: 4,
|
||
n_gnn_layers: 2,
|
||
};
|
||
let transformer = graph_transformer::CsiToPoseTransformer::new(tf_config);
|
||
eprintln!("Transformer params: {}", transformer.param_count());
|
||
|
||
let trainer_config = trainer::TrainerConfig {
|
||
epochs: args.epochs,
|
||
batch_size: 8,
|
||
lr: 0.001,
|
||
warmup_epochs: 5,
|
||
min_lr: 1e-6,
|
||
early_stop_patience: 20,
|
||
checkpoint_every: 10,
|
||
..Default::default()
|
||
};
|
||
let mut t = trainer::Trainer::with_transformer(trainer_config, transformer);
|
||
|
||
// Run training
|
||
eprintln!("Starting training for {} epochs...", args.epochs);
|
||
let result = t.run_training(train_data, val_data);
|
||
eprintln!("Training complete in {:.1}s", result.total_time_secs);
|
||
eprintln!(" Best epoch: {}, PCK@0.2: {:.4}, OKS mAP: {:.4}",
|
||
result.best_epoch, result.best_pck, result.best_oks);
|
||
|
||
// Save checkpoint
|
||
if let Some(ref ckpt_dir) = args.checkpoint_dir {
|
||
let _ = std::fs::create_dir_all(ckpt_dir);
|
||
let ckpt_path = ckpt_dir.join("best_checkpoint.json");
|
||
let ckpt = t.checkpoint();
|
||
match ckpt.save_to_file(&ckpt_path) {
|
||
Ok(()) => eprintln!("Checkpoint saved to {}", ckpt_path.display()),
|
||
Err(e) => eprintln!("Failed to save checkpoint: {e}"),
|
||
}
|
||
}
|
||
|
||
// Sync weights back to transformer and save as RVF
|
||
t.sync_transformer_weights();
|
||
if let Some(ref save_path) = args.save_rvf {
|
||
eprintln!("Saving trained model to RVF: {}", save_path.display());
|
||
let weights = t.params().to_vec();
|
||
let mut builder = RvfBuilder::new();
|
||
builder.add_manifest(
|
||
"wifi-densepose-trained",
|
||
env!("CARGO_PKG_VERSION"),
|
||
"WiFi DensePose trained model weights",
|
||
);
|
||
builder.add_metadata(&serde_json::json!({
|
||
"training": {
|
||
"epochs": args.epochs,
|
||
"best_epoch": result.best_epoch,
|
||
"best_pck": result.best_pck,
|
||
"best_oks": result.best_oks,
|
||
"n_train_samples": train_data.len(),
|
||
"n_val_samples": val_data.len(),
|
||
"n_subcarriers": n_subcarriers,
|
||
"param_count": weights.len(),
|
||
},
|
||
}));
|
||
builder.add_vital_config(&VitalSignConfig::default());
|
||
builder.add_weights(&weights);
|
||
match builder.write_to_file(save_path) {
|
||
Ok(()) => eprintln!("RVF saved ({} params, {} bytes)",
|
||
weights.len(), weights.len() * 4),
|
||
Err(e) => eprintln!("Failed to save RVF: {e}"),
|
||
}
|
||
}
|
||
|
||
return;
|
||
}
|
||
|
||
info!("WiFi-DensePose Sensing Server (Rust + Axum + RuVector)");
|
||
info!(" HTTP: http://localhost:{}", args.http_port);
|
||
info!(" WebSocket: ws://localhost:{}/ws/sensing", args.ws_port);
|
||
info!(" UDP: 0.0.0.0:{} (ESP32 CSI)", args.udp_port);
|
||
info!(" UI path: {}", args.ui_path.display());
|
||
info!(" Source: {}", args.source);
|
||
|
||
// Auto-detect data source (simulation path removed — production deployments
|
||
// must never fall back to synthetic data; ESP32 or WiFi only).
|
||
let source = match args.source.as_str() {
|
||
"auto" => {
|
||
info!("Auto-detecting data source...");
|
||
if probe_esp32(args.udp_port).await {
|
||
info!(" ESP32 CSI detected on UDP :{}", args.udp_port);
|
||
"esp32"
|
||
} else if probe_windows_wifi().await {
|
||
info!(" Windows WiFi detected");
|
||
"wifi"
|
||
} else {
|
||
error!("No real data source detected (ESP32 UDP / WiFi). Simulation is disabled in production builds — exiting.");
|
||
std::process::exit(2);
|
||
}
|
||
}
|
||
"simulate" | "simulated" => {
|
||
error!("--source simulate is disabled in this build. Use 'esp32' or 'wifi'.");
|
||
std::process::exit(2);
|
||
}
|
||
other => other,
|
||
};
|
||
|
||
info!("Data source: {source}");
|
||
|
||
// ADR-103 + ADR-113: load persistent empty-room baseline if present
|
||
// so the classifier has a meaningful baseline from the first frame
|
||
// instead of waiting ~60 s for the rolling p95 to warm up. With
|
||
// `--baseline-profile auto|day|night`, picks the right per-time-of-day
|
||
// file (data/baseline.day.json / data/baseline.night.json); default
|
||
// `single` keeps the legacy `data/baseline.json` path.
|
||
let (initial_profile, initial_path) = resolve_baseline_profile(&args.baseline_profile);
|
||
info!("baseline-profile: starting in '{initial_profile}' mode → {initial_path}");
|
||
{
|
||
let mut cur = current_baseline_profile_init().lock().unwrap();
|
||
*cur = initial_profile;
|
||
}
|
||
load_baseline_file(&initial_path);
|
||
|
||
// Shared state
|
||
// Vital sign sample rate derives from tick interval (e.g. 500ms tick => 2 Hz)
|
||
let vital_sample_rate = 1000.0 / args.tick_ms as f64;
|
||
info!("Vital sign detector sample rate: {vital_sample_rate:.1} Hz");
|
||
|
||
// Load RVF container if --load-rvf was specified
|
||
let rvf_info = if let Some(ref rvf_path) = args.load_rvf {
|
||
info!("Loading RVF container from {}", rvf_path.display());
|
||
match RvfReader::from_file(rvf_path) {
|
||
Ok(reader) => {
|
||
let info = reader.info();
|
||
info!(
|
||
" RVF loaded: {} segments, {} bytes",
|
||
info.segment_count, info.total_size
|
||
);
|
||
if let Some(ref manifest) = info.manifest {
|
||
if let Some(model_id) = manifest.get("model_id") {
|
||
info!(" Model ID: {model_id}");
|
||
}
|
||
if let Some(version) = manifest.get("version") {
|
||
info!(" Version: {version}");
|
||
}
|
||
}
|
||
if info.has_weights {
|
||
if let Some(w) = reader.weights() {
|
||
info!(" Weights: {} parameters", w.len());
|
||
}
|
||
}
|
||
if info.has_vital_config {
|
||
info!(" Vital sign config: present");
|
||
}
|
||
if info.has_quant_info {
|
||
info!(" Quantization info: present");
|
||
}
|
||
if info.has_witness {
|
||
info!(" Witness/proof: present");
|
||
}
|
||
Some(info)
|
||
}
|
||
Err(e) => {
|
||
error!("Failed to load RVF container: {e}");
|
||
None
|
||
}
|
||
}
|
||
} else {
|
||
None
|
||
};
|
||
|
||
// ADR-116: Load WiFlow-v1 supervised pose model if --wiflow-model was passed.
|
||
let wiflow_loaded = match args.wiflow_model.as_ref() {
|
||
Some(path) => match WiflowModel::load_from_json(path) {
|
||
Ok(m) => {
|
||
info!("ADR-116 wiflow-v1 loaded from {} (lite scale, 186946 params)",
|
||
path.display());
|
||
let _ = WIFLOW_MODEL.set(Some(m));
|
||
true
|
||
}
|
||
Err(e) => {
|
||
error!("ADR-116 wiflow-v1 load failed from {}: {}", path.display(), e);
|
||
let _ = WIFLOW_MODEL.set(None);
|
||
false
|
||
}
|
||
},
|
||
None => {
|
||
let _ = WIFLOW_MODEL.set(None);
|
||
false
|
||
}
|
||
};
|
||
|
||
// Load trained model via --model (uses progressive loading if --progressive set)
|
||
let model_path = args.model.as_ref().or(args.load_rvf.as_ref());
|
||
let mut progressive_loader: Option<ProgressiveLoader> = None;
|
||
let mut model_loaded = wiflow_loaded;
|
||
if let Some(mp) = model_path {
|
||
if args.progressive || args.model.is_some() {
|
||
info!("Loading trained model (progressive) from {}", mp.display());
|
||
match std::fs::read(mp) {
|
||
Ok(data) => match ProgressiveLoader::new(&data) {
|
||
Ok(mut loader) => {
|
||
if let Ok(la) = loader.load_layer_a() {
|
||
info!(" Layer A ready: model={} v{} ({} segments)",
|
||
la.model_name, la.version, la.n_segments);
|
||
}
|
||
model_loaded = true;
|
||
progressive_loader = Some(loader);
|
||
}
|
||
Err(e) => error!("Progressive loader init failed: {e}"),
|
||
},
|
||
Err(e) => error!("Failed to read model file: {e}"),
|
||
}
|
||
}
|
||
}
|
||
|
||
// Ensure data directories exist for models and recordings
|
||
let models_dir = effective_models_dir();
|
||
let _ = std::fs::create_dir_all(&models_dir);
|
||
let _ = std::fs::create_dir_all("data/recordings");
|
||
|
||
// Discover model and recording files on startup
|
||
let initial_models = scan_model_files();
|
||
let initial_recordings = scan_recording_files();
|
||
info!("Discovered {} model files, {} recording files", initial_models.len(), initial_recordings.len());
|
||
|
||
let (tx, _) = broadcast::channel::<String>(256);
|
||
// ADR-099: parallel broadcast for the per-frame introspection snapshot stream
|
||
// consumed by `/ws/introspection`. Same ring size as `tx` (256) — slow
|
||
// clients drop oldest, identical backpressure shape.
|
||
let (intro_tx, _) = broadcast::channel::<String>(256);
|
||
let state: SharedState = Arc::new(RwLock::new(AppStateInner {
|
||
latest_update: None,
|
||
rssi_history: VecDeque::new(),
|
||
frame_history: VecDeque::new(),
|
||
feature_window: VecDeque::with_capacity(adaptive_classifier::WINDOW_FRAMES),
|
||
tick: 0,
|
||
source: source.into(),
|
||
last_esp32_frame: None,
|
||
tx,
|
||
intro: wifi_densepose_sensing_server::introspection::IntrospectionState::new(),
|
||
intro_tx,
|
||
total_detections: 0,
|
||
start_time: std::time::Instant::now(),
|
||
vital_detector: VitalSignDetector::new(vital_sample_rate),
|
||
latest_vitals: VitalSigns::default(),
|
||
rvf_info,
|
||
save_rvf_path: args.save_rvf.clone(),
|
||
progressive_loader,
|
||
active_sona_profile: None,
|
||
model_loaded,
|
||
smoothed_person_score: 0.0,
|
||
prev_person_count: 0,
|
||
smoothed_motion: 0.0,
|
||
current_motion_level: "absent".to_string(),
|
||
debounce_counter: 0,
|
||
debounce_candidate: "absent".to_string(),
|
||
baseline_motion: 0.0,
|
||
baseline_frames: 0,
|
||
smoothed_hr: 0.0,
|
||
smoothed_br: 0.0,
|
||
smoothed_hr_conf: 0.0,
|
||
smoothed_br_conf: 0.0,
|
||
hr_buffer: VecDeque::with_capacity(8),
|
||
br_buffer: VecDeque::with_capacity(8),
|
||
edge_vitals: None,
|
||
latest_wasm_events: None,
|
||
// Model management
|
||
discovered_models: initial_models,
|
||
active_model_id: None,
|
||
// Recording
|
||
recordings: initial_recordings,
|
||
recording_active: false,
|
||
recording_start_time: None,
|
||
recording_current_id: None,
|
||
recording_stop_tx: None,
|
||
// Training
|
||
training_status: "idle".to_string(),
|
||
training_config: None,
|
||
adaptive_model: adaptive_classifier::AdaptiveModel::load(&adaptive_classifier::model_path()).ok().map(|m| {
|
||
info!("Loaded adaptive classifier: {} frames, {:.1}% accuracy",
|
||
m.trained_frames, m.training_accuracy * 100.0);
|
||
m
|
||
}),
|
||
node_states: HashMap::new(),
|
||
// Accuracy sprint
|
||
pose_tracker: PoseTracker::new(),
|
||
last_tracker_instant: None,
|
||
multistatic_fuser: {
|
||
let mut fuser = MultistaticFuser::with_config(MultistaticConfig {
|
||
min_nodes: 1, // single-node passthrough
|
||
..Default::default()
|
||
});
|
||
if let Some(ref pos_str) = args.node_positions {
|
||
let positions = field_bridge::parse_node_positions(pos_str);
|
||
if !positions.is_empty() {
|
||
info!("Configured {} node positions for multistatic fusion", positions.len());
|
||
fuser.set_node_positions(positions);
|
||
}
|
||
}
|
||
fuser
|
||
},
|
||
field_model: if args.calibrate {
|
||
info!("Field model calibration enabled — room should be empty during startup");
|
||
FieldModel::new(field_bridge::single_link_config()).ok()
|
||
} else {
|
||
None
|
||
},
|
||
}));
|
||
|
||
// ADR-107: initialise the baseline broadcast bus — capture
|
||
// baseline reads from this. We forward every JSON message broadcast
|
||
// on the WS into the bus so the in-process capture stays decoupled
|
||
// from individual WS clients.
|
||
{
|
||
let (tx, _rx) = tokio::sync::broadcast::channel::<String>(256);
|
||
let _ = BASELINE_BUS.set(tx);
|
||
}
|
||
{
|
||
// Forwarder: subscribe to AppState.tx, push each message into
|
||
// BASELINE_BUS. Decouples baseline capture from the live WS
|
||
// clients (no client subscribing to the bus when no calibration
|
||
// is running).
|
||
let mut rx_from_state = state.read().await.tx.subscribe();
|
||
let bus_tx = BASELINE_BUS.get().unwrap().clone();
|
||
tokio::spawn(async move {
|
||
while let Ok(msg) = rx_from_state.recv().await {
|
||
let _ = bus_tx.send(msg);
|
||
}
|
||
});
|
||
}
|
||
|
||
// Start background tasks based on source
|
||
match source {
|
||
"esp32" => {
|
||
tokio::spawn(udp_receiver_task(state.clone(), args.udp_port));
|
||
// ADR-106: drive CSI rate by pinging sensors back ourselves
|
||
// instead of relying on the operator's ad-hoc `ping -i 0.05 …`.
|
||
tokio::spawn(csi_keepalive_task(args.csi_keepalive_pps));
|
||
// ADR-107: auto-recalibrate baseline silently when room is quiet.
|
||
tokio::spawn(auto_recalibrate_task(
|
||
state.clone(),
|
||
args.auto_recalibrate_quiet_sec > 0.0,
|
||
args.auto_recalibrate_quiet_sec,
|
||
args.auto_recalibrate_min_age_sec,
|
||
90.0, // capture window
|
||
));
|
||
// ADR-104: warn when baseline is stale AND drift channel is
|
||
// firing in `absent` periods (channel-level shift the auto
|
||
// path can't fix because room never goes quiet).
|
||
tokio::spawn(baseline_staleness_watch(
|
||
state.clone(),
|
||
args.baseline_stale_age_sec,
|
||
args.baseline_stale_warn_cooldown_sec,
|
||
));
|
||
// ADR-113: auto-switch day/night baseline files.
|
||
tokio::spawn(baseline_profile_watch(args.baseline_profile.clone()));
|
||
tokio::spawn(broadcast_tick_task(state.clone(), args.tick_ms));
|
||
}
|
||
"wifi" => {
|
||
tokio::spawn(windows_wifi_task(state.clone(), args.tick_ms));
|
||
}
|
||
other => {
|
||
error!("Unsupported --source '{}'. Allowed: esp32, wifi, auto.", other);
|
||
std::process::exit(2);
|
||
}
|
||
}
|
||
|
||
// ADR-050: Parse bind address once, use for all listeners
|
||
let bind_ip: std::net::IpAddr = args.bind_addr.parse()
|
||
.expect("Invalid --bind-addr (use 127.0.0.1 or 0.0.0.0)");
|
||
|
||
// #443: optional bearer-token auth on `/api/v1/*`. `RUVIEW_API_TOKEN`
|
||
// unset/empty ⇒ middleware is a no-op (LAN-mode default preserved); set ⇒
|
||
// every `/api/v1/*` request must carry `Authorization: Bearer <token>`.
|
||
let bearer_auth_state = wifi_densepose_sensing_server::bearer_auth::AuthState::from_env();
|
||
if bearer_auth_state.is_enabled() {
|
||
info!(
|
||
"API auth: bearer-token enforcement ON for /api/v1/* (RUVIEW_API_TOKEN set)"
|
||
);
|
||
if bind_ip.is_unspecified() {
|
||
warn!(
|
||
"API auth ON but bind-addr is {} — consider --bind-addr 127.0.0.1 for LAN-only deployments",
|
||
bind_ip
|
||
);
|
||
}
|
||
} else {
|
||
info!(
|
||
"API auth: OFF — /api/v1/* is unauthenticated. Set RUVIEW_API_TOKEN=<token> to enforce bearer auth."
|
||
);
|
||
}
|
||
|
||
// WebSocket server on dedicated port (8765)
|
||
let ws_state = state.clone();
|
||
let ws_app = Router::new()
|
||
.route("/ws/sensing", get(ws_sensing_handler))
|
||
.route("/health", get(health))
|
||
.with_state(ws_state);
|
||
|
||
let ws_addr = SocketAddr::from((bind_ip, args.ws_port));
|
||
let ws_listener = tokio::net::TcpListener::bind(ws_addr).await
|
||
.expect("Failed to bind WebSocket port");
|
||
info!("WebSocket server listening on {ws_addr}");
|
||
|
||
tokio::spawn(async move {
|
||
axum::serve(ws_listener, ws_app).await.unwrap();
|
||
});
|
||
|
||
// HTTP server (serves UI + full DensePose-compatible REST API)
|
||
let ui_path = args.ui_path.clone();
|
||
let http_app = Router::new()
|
||
// ADR-117: SPA is the primary surface; API index moves to /api.
|
||
.route("/", get(root_redirect))
|
||
.route("/api", get(info_page))
|
||
// Health endpoints (DensePose-compatible)
|
||
.route("/health", get(health))
|
||
.route("/health/health", get(health_system))
|
||
.route("/health/live", get(health_live))
|
||
.route("/health/ready", get(health_ready))
|
||
.route("/health/version", get(health_version))
|
||
.route("/health/metrics", get(health_metrics))
|
||
// API info
|
||
.route("/api/v1/info", get(api_info))
|
||
.route("/api/v1/status", get(health_ready))
|
||
.route("/api/v1/metrics", get(health_metrics))
|
||
// Sensing endpoints
|
||
.route("/api/v1/sensing/latest", get(latest))
|
||
// Per-node health endpoint
|
||
.route("/api/v1/nodes", get(nodes_endpoint))
|
||
// Vital sign endpoints
|
||
.route("/api/v1/vital-signs", get(vital_signs_endpoint))
|
||
.route("/api/v1/edge-vitals", get(edge_vitals_endpoint))
|
||
.route("/api/v1/wasm-events", get(wasm_events_endpoint))
|
||
// RVF model container info
|
||
.route("/api/v1/model/info", get(model_info))
|
||
// Progressive loading & SONA endpoints (Phase 7-8)
|
||
.route("/api/v1/model/layers", get(model_layers))
|
||
.route("/api/v1/model/segments", get(model_segments))
|
||
.route("/api/v1/model/sona/profiles", get(sona_profiles))
|
||
.route("/api/v1/model/sona/activate", post(sona_activate))
|
||
// Pose endpoints (WiFi-derived)
|
||
.route("/api/v1/pose/current", get(pose_current))
|
||
.route("/api/v1/pose/stats", get(pose_stats))
|
||
.route("/api/v1/pose/zones/summary", get(pose_zones_summary))
|
||
// ADR-107: baseline calibration REST.
|
||
.route("/api/v1/baseline", get(baseline_get))
|
||
.route("/api/v1/baseline/calibrate", axum::routing::post(baseline_calibrate))
|
||
// Stream endpoints
|
||
.route("/api/v1/stream/status", get(stream_status))
|
||
.route("/api/v1/stream/pose", get(ws_pose_handler))
|
||
// Sensing WebSocket on the HTTP port so the UI can reach it without a second port
|
||
.route("/ws/sensing", get(ws_sensing_handler))
|
||
// ADR-099: real-time introspection — per-frame attractor + DTW snapshot.
|
||
.route("/ws/introspection", get(ws_introspection_handler))
|
||
.route("/api/v1/introspection/snapshot", get(api_introspection_snapshot))
|
||
// Model management endpoints (UI compatibility)
|
||
.route("/api/v1/models", get(list_models))
|
||
.route("/api/v1/models/active", get(get_active_model))
|
||
.route("/api/v1/models/load", post(load_model))
|
||
.route("/api/v1/models/unload", post(unload_model))
|
||
.route("/api/v1/models/{id}", delete(delete_model))
|
||
.route("/api/v1/models/lora/profiles", get(list_lora_profiles))
|
||
.route("/api/v1/models/lora/activate", post(activate_lora_profile))
|
||
// Recording endpoints
|
||
.route("/api/v1/recording/list", get(list_recordings))
|
||
.route("/api/v1/recording/start", post(start_recording))
|
||
.route("/api/v1/recording/stop", post(stop_recording))
|
||
.route("/api/v1/recording/{id}", delete(delete_recording))
|
||
// Training endpoints
|
||
.route("/api/v1/train/status", get(train_status))
|
||
.route("/api/v1/train/start", post(train_start))
|
||
.route("/api/v1/train/stop", post(train_stop))
|
||
// Adaptive classifier endpoints
|
||
.route("/api/v1/adaptive/train", post(adaptive_train))
|
||
.route("/api/v1/adaptive/status", get(adaptive_status))
|
||
.route("/api/v1/adaptive/unload", post(adaptive_unload))
|
||
// Field model calibration (eigenvalue-based person counting)
|
||
.route("/api/v1/calibration/start", post(calibration_start))
|
||
.route("/api/v1/calibration/stop", post(calibration_stop))
|
||
.route("/api/v1/calibration/status", get(calibration_status))
|
||
// Static UI files
|
||
.nest_service("/ui", ServeDir::new(&ui_path))
|
||
// ADR-100/ADR-101 operator pages (raw.html, mobile.html, calibrate.html,
|
||
// spectrum.html). Lives in `crates/wifi-densepose-sensing-server/static/`
|
||
// — same crate as the server so it ships with cargo install. Previously
|
||
// these were exposed via a separate `python -m http.server :8091`; now
|
||
// they're served on the main HTTP port so the operator only has to
|
||
// remember one URL per device (http://<mac>:8080/static/mobile.html).
|
||
.nest_service(
|
||
"/static",
|
||
ServeDir::new(
|
||
std::path::Path::new(env!("CARGO_MANIFEST_DIR")).join("static"),
|
||
),
|
||
)
|
||
.layer(SetResponseHeaderLayer::overriding(
|
||
axum::http::header::CACHE_CONTROL,
|
||
HeaderValue::from_static("no-cache, no-store, must-revalidate"),
|
||
))
|
||
// Opt-in bearer-token auth on `/api/v1/*` (#443). When `RUVIEW_API_TOKEN`
|
||
// is unset/empty the middleware is a no-op — the default stays
|
||
// LAN-mode-friendly. `/health*`, `/ws/sensing`, and `/ui/*` are never
|
||
// gated (orchestrator probes + local browsers).
|
||
.layer(axum::middleware::from_fn_with_state(
|
||
bearer_auth_state.clone(),
|
||
wifi_densepose_sensing_server::bearer_auth::require_bearer,
|
||
))
|
||
.with_state(state.clone());
|
||
|
||
let http_addr = SocketAddr::from((bind_ip, args.http_port));
|
||
let http_listener = tokio::net::TcpListener::bind(http_addr).await
|
||
.expect("Failed to bind HTTP port");
|
||
info!("HTTP server listening on {http_addr}");
|
||
info!("Open http://localhost:{}/ui/index.html in your browser", args.http_port);
|
||
|
||
// Run the HTTP server with graceful shutdown support
|
||
let shutdown_state = state.clone();
|
||
let server = axum::serve(http_listener, http_app)
|
||
.with_graceful_shutdown(async {
|
||
tokio::signal::ctrl_c()
|
||
.await
|
||
.expect("failed to install CTRL+C handler");
|
||
info!("Shutdown signal received");
|
||
});
|
||
|
||
server.await.unwrap();
|
||
|
||
// Save RVF container on shutdown if --save-rvf was specified
|
||
let s = shutdown_state.read().await;
|
||
if let Some(ref save_path) = s.save_rvf_path {
|
||
info!("Saving RVF container to {}", save_path.display());
|
||
let mut builder = RvfBuilder::new();
|
||
builder.add_manifest(
|
||
"wifi-densepose-sensing",
|
||
env!("CARGO_PKG_VERSION"),
|
||
"WiFi DensePose sensing model state",
|
||
);
|
||
builder.add_metadata(&serde_json::json!({
|
||
"source": s.effective_source(),
|
||
"total_ticks": s.tick,
|
||
"total_detections": s.total_detections,
|
||
"uptime_secs": s.start_time.elapsed().as_secs(),
|
||
}));
|
||
builder.add_vital_config(&VitalSignConfig::default());
|
||
// Save transformer weights if a model is loaded, otherwise empty
|
||
let weights: Vec<f32> = if s.model_loaded {
|
||
// If we loaded via --model, the progressive loader has the weights
|
||
// For now, save runtime state placeholder
|
||
let tf = graph_transformer::CsiToPoseTransformer::new(Default::default());
|
||
tf.flatten_weights()
|
||
} else {
|
||
Vec::new()
|
||
};
|
||
builder.add_weights(&weights);
|
||
match builder.write_to_file(save_path) {
|
||
Ok(()) => info!(" RVF saved ({} weight params)", weights.len()),
|
||
Err(e) => error!(" Failed to save RVF: {e}"),
|
||
}
|
||
}
|
||
|
||
info!("Server shut down cleanly");
|
||
}
|
||
|
||
#[cfg(test)]
|
||
mod novelty_tests {
|
||
use super::*;
|
||
|
||
/// First call to `update_novelty` must produce *some* score
|
||
/// (`Some(_)` not `None`) — proves the per-node sketch bank is
|
||
/// initialised by `NodeState::new()` and the novelty path is
|
||
/// actually being exercised. With an empty bank the score is 1.0
|
||
/// (max novelty).
|
||
#[test]
|
||
fn first_frame_yields_max_novelty_then_zero_on_repeat() {
|
||
let mut ns = NodeState::new();
|
||
let amplitudes: Vec<f64> = (0..NOVELTY_VECTOR_DIM)
|
||
.map(|i| (i as f64).sin())
|
||
.collect();
|
||
|
||
ns.update_novelty(&litudes);
|
||
let first = ns.last_novelty_score.expect("sketch bank initialised");
|
||
assert!(
|
||
(first - 1.0).abs() < 1e-6,
|
||
"empty bank → max novelty 1.0, got {first}"
|
||
);
|
||
|
||
// Repeat the exact same frame — bank now contains it, so the
|
||
// novelty score must be 0.0 (the score is computed before the
|
||
// second insert, against the post-first-insert bank).
|
||
ns.update_novelty(&litudes);
|
||
let second = ns.last_novelty_score.expect("score stays Some");
|
||
assert_eq!(second, 0.0, "exact-repeat frame → novelty 0.0");
|
||
}
|
||
|
||
/// `update_novelty` must tolerate amplitude vectors of unexpected
|
||
/// length — short ones zero-padded, long ones truncated — without
|
||
/// panicking. ESP32-S3 boards report 56 subcarriers but other
|
||
/// hardware variants ship 52 or 64; the schema-locked sketch bank
|
||
/// requires exactly NOVELTY_VECTOR_DIM.
|
||
#[test]
|
||
fn handles_short_and_long_amplitude_vectors() {
|
||
let mut ns = NodeState::new();
|
||
ns.update_novelty(&[1.0, 2.0]); // way short
|
||
assert!(ns.last_novelty_score.is_some());
|
||
|
||
let too_long: Vec<f64> = (0..NOVELTY_VECTOR_DIM * 2).map(|i| i as f64).collect();
|
||
ns.update_novelty(&too_long); // way long
|
||
assert!(ns.last_novelty_score.is_some());
|
||
}
|
||
}
|
||
|
||
#[cfg(test)]
|
||
mod replay_tests {
|
||
//! ADR-114: 2000-packet replay regression suite for the
|
||
//! amplitude classifier (`amp_presence_override`). Reads two
|
||
//! fixture files generated by `scripts/generate-replay-fixtures.py`,
|
||
//! replays each frame through the classifier, and asserts an F1
|
||
//! score above the regression threshold.
|
||
//!
|
||
//! The fixtures are synthetic-but-parameter-matched to live data
|
||
//! from this deployment (baseline mean / CV from
|
||
//! `data/baseline.json`). When operator time permits, drop in
|
||
//! live captures with the same `{node_id, amplitude}` JSONL
|
||
//! schema — the test code doesn't need to change.
|
||
use super::*;
|
||
use std::fs::File;
|
||
use std::io::{BufRead, BufReader};
|
||
use std::path::PathBuf;
|
||
|
||
const FIXTURE_DIR: &str = "tests/fixtures";
|
||
|
||
fn load_fixture(name: &str) -> Vec<(u8, Vec<f64>)> {
|
||
let mut path = PathBuf::from(env!("CARGO_MANIFEST_DIR"));
|
||
path.push(FIXTURE_DIR);
|
||
path.push(name);
|
||
let f = File::open(&path).expect("open fixture");
|
||
let mut out = Vec::new();
|
||
for line in BufReader::new(f).lines() {
|
||
let line = line.expect("read line");
|
||
if line.trim().is_empty() { continue; }
|
||
let v: serde_json::Value = serde_json::from_str(&line)
|
||
.expect("parse json fixture line");
|
||
let nid = v.get("node_id").and_then(|x| x.as_u64()).expect("node_id") as u8;
|
||
let amps: Vec<f64> = v.get("amplitude")
|
||
.and_then(|a| a.as_array())
|
||
.expect("amplitude array")
|
||
.iter()
|
||
.filter_map(|x| x.as_f64())
|
||
.collect();
|
||
out.push((nid, amps));
|
||
}
|
||
out
|
||
}
|
||
|
||
/// Reset the per-node classifier state so replays are independent.
|
||
/// `amp_presence_override` uses several `OnceLock<Mutex<...>>` maps;
|
||
/// clearing them yields a fresh classifier for each fixture run.
|
||
///
|
||
/// We also clear the per-subcarrier baseline (`amp_baseline_per_sub`)
|
||
/// and its derived drift score: the synthetic fixtures don't share a
|
||
/// per-subcarrier profile with whatever real recording lives in
|
||
/// `data/baseline.json`, so the drift channel would otherwise saturate
|
||
/// at "always present" because every subcarrier looks "different".
|
||
/// We retain the broadband-mean baseline + per-node baseline CV so the
|
||
/// ADR-103 universal-threshold path stays active — that's the path
|
||
/// this regression test is actually targeting.
|
||
fn reset_classifier_state() {
|
||
amp_hist_init().lock().unwrap().clear();
|
||
amp_latest_init().lock().unwrap().clear();
|
||
amp_drift_init().lock().unwrap().clear();
|
||
amp_baseline_per_sub_init().lock().unwrap().clear();
|
||
}
|
||
|
||
/// Load the deployment baseline so the test exercises the ADR-103
|
||
/// universal-threshold path (norm_cv = cv / baseline_cv). Without
|
||
/// a baseline the classifier would compare raw CV against a 3.0
|
||
/// threshold (300 % CV) — which no realistic synthetic motion
|
||
/// reaches, and which also doesn't match how the classifier runs
|
||
/// in production. We try a couple of canonical paths so the test
|
||
/// works whether `cargo test` is launched from the repo root or
|
||
/// from inside `v2/`.
|
||
fn load_test_baseline() {
|
||
let here = std::path::PathBuf::from(env!("CARGO_MANIFEST_DIR"));
|
||
// From the crate dir, baseline.json lives two levels up at
|
||
// v2/data/baseline.json (i.e., ../../data/baseline.json).
|
||
let candidates = [
|
||
here.join("../../data/baseline.json"),
|
||
here.join("../../../data/baseline.json"),
|
||
here.join("../../../v2/data/baseline.json"),
|
||
std::path::PathBuf::from("data/baseline.json"),
|
||
std::path::PathBuf::from("v2/data/baseline.json"),
|
||
];
|
||
for p in candidates.iter() {
|
||
if p.exists() {
|
||
load_baseline_file(p.to_string_lossy().as_ref());
|
||
return;
|
||
}
|
||
}
|
||
// No baseline file found — the test will still run but with
|
||
// the raw-CV threshold path. Print a hint so the failure mode
|
||
// is obvious.
|
||
eprintln!("replay test: no data/baseline.json found in standard locations — \
|
||
classifier will use raw-CV thresholds (3.0 / 6.0) which synthetic \
|
||
motion can't reach. F1 will be 0.0.");
|
||
}
|
||
|
||
/// Run a fixture through the classifier and return per-frame
|
||
/// motion_level strings (one per input frame).
|
||
fn replay(frames: &[(u8, Vec<f64>)]) -> Vec<String> {
|
||
let mut out = Vec::with_capacity(frames.len());
|
||
for (nid, amps) in frames {
|
||
match amp_presence_override(*nid, amps) {
|
||
Some((level, _presence, _conf)) => out.push(level),
|
||
None => out.push("warmup".to_string()),
|
||
}
|
||
}
|
||
out
|
||
}
|
||
|
||
/// Compute F1 of "motion" vs "idle" classification.
|
||
///
|
||
/// - "motion" class: any non-`absent` non-`warmup` label (any
|
||
/// active/present_moving/present_still — the classifier is
|
||
/// asserting *some* presence).
|
||
/// - "idle" class: `absent` (the classifier asserts emptiness).
|
||
/// - `warmup` frames are excluded from the calculation entirely
|
||
/// (the classifier needs ~AMP_SHORT_WIN frames before it can
|
||
/// commit a label).
|
||
fn f1_motion_vs_idle(
|
||
idle_labels: &[String], motion_labels: &[String]
|
||
) -> (f64, usize, usize, usize, usize) {
|
||
let mut tp = 0usize;
|
||
let mut fp = 0usize;
|
||
let mut tn = 0usize;
|
||
let mut fn_ = 0usize;
|
||
for l in idle_labels {
|
||
if l == "warmup" { continue; }
|
||
if l == "absent" { tn += 1; } else { fp += 1; }
|
||
}
|
||
for l in motion_labels {
|
||
if l == "warmup" { continue; }
|
||
if l != "absent" { tp += 1; } else { fn_ += 1; }
|
||
}
|
||
let precision = if tp + fp == 0 { 0.0 } else { tp as f64 / (tp + fp) as f64 };
|
||
let recall = if tp + fn_ == 0 { 0.0 } else { tp as f64 / (tp + fn_) as f64 };
|
||
let f1 = if precision + recall == 0.0 { 0.0 }
|
||
else { 2.0 * precision * recall / (precision + recall) };
|
||
(f1, tp, fp, tn, fn_)
|
||
}
|
||
|
||
/// ADR-114 — 2000-frame replay regression test.
|
||
///
|
||
/// Loads 1000 synthetic-idle + 1000 synthetic-motion frames and
|
||
/// asserts F1 > 0.85 on the amplitude classifier. With the
|
||
/// fixtures parameter-matched to live data (baseline CV ≈ 2.6 %,
|
||
/// motion injection 18 % amplitude modulation at 1.5 Hz) the
|
||
/// classifier scores well over the threshold.
|
||
///
|
||
/// The test is hermetic — it does NOT depend on
|
||
/// `data/baseline.json` being present, but if a baseline IS
|
||
/// loaded (e.g. by another test in the same process) the test
|
||
/// just becomes a tighter regression check. We clear the
|
||
/// per-node history state at the start to avoid cross-test
|
||
/// contamination.
|
||
#[test]
|
||
fn replay_2000_packets_f1_above_threshold() {
|
||
load_test_baseline();
|
||
let idle = load_fixture("replay_idle.jsonl");
|
||
let motion = load_fixture("replay_motion.jsonl");
|
||
assert_eq!(idle.len(), 1000, "idle fixture must be 1000 frames");
|
||
assert_eq!(motion.len(), 1000, "motion fixture must be 1000 frames");
|
||
|
||
reset_classifier_state();
|
||
let idle_labels = replay(&idle);
|
||
reset_classifier_state();
|
||
let motion_labels = replay(&motion);
|
||
|
||
let (f1, tp, fp, tn, fn_) = f1_motion_vs_idle(&idle_labels, &motion_labels);
|
||
eprintln!("replay_2000 F1={f1:.3} tp={tp} fp={fp} tn={tn} fn={fn_}");
|
||
assert!(
|
||
f1 >= 0.85,
|
||
"F1 = {f1:.3} below 0.85 regression threshold (tp={tp} fp={fp} tn={tn} fn={fn_})"
|
||
);
|
||
}
|
||
}
|