wifi-densepose/v2/crates/wifi-densepose-sensing-server
ruv ca97527646 feat(introspection): I6 — regime-changed signal + per-frame analyze + honest ADR-099 D8 amendment
Three threads in this commit:

1) Per-frame attractor analysis (default analyze_every_n: 8 → 1).
   The I5 benchmark put per-frame update at 0.012 ms p99 — 83× under D4's
   1 ms budget. The cost case for the every-8th-frame default doesn't hold;
   per-frame analysis is what makes regime_changed a viable early-detection
   trigger.

2) New `regime_changed: bool` field in IntrospectionSnapshot — flips on any
   frame whose attractor regime classification differs from the previous
   frame's. Pairs with top_k_similarity (full-shape match) to give
   downstream consumers two latencies with different robustness profiles.

3) Honest amendment of ADR-099 D8 to reflect empirical reality:
   - L1 stand-in achieves 3.20× ratio (5-frame shape match vs 16-frame
     event-path floor); the 10× aspirational bar is architecturally
     unreachable at 1-D scalar feature resolution.
   - regime_changed didn't fire in the 10-frame motion window — the
     200-frame noise trajectory dominates the Lyapunov classification, and
     short perturbations don't shift the regime fast enough on a scalar
     feature.
   - Path to 10×: ADR-208 Phase 2 (Hailo NPU vec128 embeddings) — multi-dim
     partial matches discriminate from noise in 1-2 frames, not 5.
   - Side finding: midstream temporal-compare::DTW uses *discrete equality*
     cost (designed for LLM tokens), not numeric distance — swapping it in
     for f64 amplitude scoring would be strictly worse than the L1 stand-in.
     A numeric DTW is a separate concern (hand-roll or new crate).
   - Revised D8: ship behind --introspection (off by default) until multi-
     dim features land. Per-frame update budget IS met (0.041 ms p99 in this
     bench, ~24× under the 1 ms bar) — the feature is cheap enough to
     carry dark today.

cargo test -p wifi-densepose-sensing-server --no-default-features:
  introspection (lib): 8 passed, 0 failed
  introspection_latency (test): 5 passed, 0 failed (incl. new
                                 regime_change_path_latency)
clippy: clean on the introspection surface (pre-existing approx_constant
        lints in pose.rs / main.rs unchanged).

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-13 23:29:37 -04:00
..
src feat(introspection): I6 — regime-changed signal + per-frame analyze + honest ADR-099 D8 amendment 2026-05-13 23:29:37 -04:00
tests feat(introspection): I6 — regime-changed signal + per-frame analyze + honest ADR-099 D8 amendment 2026-05-13 23:29:37 -04:00
Cargo.toml feat(sensing-server): introspection module skeleton (ADR-099 D1+D7+D8) 2026-05-13 22:50:58 -04:00
README.md chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427) 2026-04-25 21:28:13 -04:00

README.md

wifi-densepose-sensing-server

Crates.io Documentation License

Lightweight Axum server for real-time WiFi sensing with RuVector signal processing.

Overview

wifi-densepose-sensing-server is the operational backend for WiFi-DensePose. It receives raw CSI frames from ESP32 hardware over UDP, runs them through the RuVector-powered signal processing pipeline, and broadcasts processed sensing updates to browser clients via WebSocket. A built-in static file server hosts the sensing UI on the same port.

The crate ships both a library (wifi_densepose_sensing_server) exposing the training and inference modules, and a binary (sensing-server) that starts the full server stack.

Integrates wifi-densepose-wifiscan for multi-BSSID WiFi scanning per ADR-022 Phase 3.

Features

  • UDP CSI ingestion -- Receives ESP32 CSI frames on port 5005 and parses them into the internal CsiFrame representation.
  • Vital sign detection -- Pure-Rust FFT-based breathing rate (0.1--0.5 Hz) and heart rate (0.67--2.0 Hz) estimation from CSI amplitude time series (ADR-021).
  • RVF container -- Standalone binary container format for packaging model weights, metadata, and configuration into a single .rvf file with 64-byte aligned segments.
  • RVF pipeline -- Progressive model loading with streaming segment decoding.
  • Graph Transformer -- Cross-attention bottleneck between antenna-space CSI features and the COCO 17-keypoint body graph, followed by GCN message passing (ADR-023 Phase 2). Pure std, no ML dependencies.
  • SONA adaptation -- LoRA + EWC++ online adaptation for environment drift without catastrophic forgetting (ADR-023 Phase 5).
  • Contrastive CSI embeddings -- Self-supervised SimCLR-style pretraining with InfoNCE loss, projection head, fingerprint indexing, and cross-modal pose alignment (ADR-024).
  • Sparse inference -- Activation profiling, sparse matrix-vector multiply, INT8/FP16 quantization, and a full sparse inference engine for edge deployment (ADR-023 Phase 6).
  • Dataset pipeline -- Training dataset loading and batching.
  • Multi-BSSID scanning -- Windows netsh integration for BSSID discovery via wifi-densepose-wifiscan (ADR-022).
  • WebSocket broadcast -- Real-time sensing updates pushed to all connected clients at ws://localhost:8765/ws/sensing.
  • Static file serving -- Hosts the sensing UI on port 8080 with CORS headers.

Modules

Module Description
vital_signs Breathing and heart rate extraction via FFT spectral analysis
rvf_container RVF binary format builder and reader
rvf_pipeline Progressive model loading from RVF containers
graph_transformer Graph Transformer + GCN for CSI-to-pose estimation
trainer Training loop orchestration
dataset Training data loading and batching
sona LoRA adapters and EWC++ continual learning
sparse_inference Neuron profiling, sparse matmul, INT8/FP16 quantization
embedding Contrastive CSI embedding model and fingerprint index

Quick Start

# Build the server
cargo build -p wifi-densepose-sensing-server

# Run with default settings (HTTP :8080, UDP :5005, WS :8765)
cargo run -p wifi-densepose-sensing-server

# Run with custom ports
cargo run -p wifi-densepose-sensing-server -- \
    --http-port 9000 \
    --udp-port 5005 \
    --static-dir ./ui

Using as a library

use wifi_densepose_sensing_server::vital_signs::VitalSignDetector;

// Create a detector with 20 Hz sample rate
let mut detector = VitalSignDetector::new(20.0);

// Feed CSI amplitude samples
for amplitude in csi_amplitudes.iter() {
    detector.push_sample(*amplitude);
}

// Extract vital signs
if let Some(vitals) = detector.detect() {
    println!("Breathing: {:.1} BPM", vitals.breathing_rate_bpm);
    println!("Heart rate: {:.0} BPM", vitals.heart_rate_bpm);
}

Architecture

ESP32 ──UDP:5005──> [ CSI Receiver ]
                          |
                    [ Signal Pipeline ]
                    (vital_signs, graph_transformer, sona)
                          |
                    [ WebSocket Broadcast ]
                          |
Browser <──WS:8765── [ Axum Server :8080 ] ──> Static UI files
Crate Role
wifi-densepose-wifiscan Multi-BSSID WiFi scanning (ADR-022)
wifi-densepose-core Shared types and traits
wifi-densepose-signal CSI signal processing algorithms
wifi-densepose-hardware ESP32 hardware interfaces
wifi-densepose-wasm Browser WASM bindings for the sensing UI
wifi-densepose-train Full training pipeline with ruvector
wifi-densepose-mat Disaster detection module

License

MIT OR Apache-2.0