wifi-densepose/v2/crates/wifi-densepose-sensing-server
WilliamMalone cb5977de7e Fix antenna-regime presence over-read: de-saturate motion_score + correct CSI header offsets
After external IPEX antennas were added to the ESP32-S3 mesh nodes, a confirmed-empty
room read "present" indefinitely. Two root-cause bugs:

1. motion_score saturation. `variance_motion` and `mbp_motion` used fixed divisors
   (/10, /25) calibrated for the antenna-less regime. Antennas raised amplitudes ~5x
   and these amplitude^2 energies ~30x, pinning both terms at the 1.0 clamp — so
   raw_motion could not fall near the presence floor and the adaptive baseline
   subtraction in smooth_and_classify was defeated. Normalize both by signal power
   (mean_amp^2) — the same dimensionless sqrt-of-power-ratio form already used by
   temporal_motion_score — making motion_score amplitude-scale-invariant. This fixes
   the single shared extract_features_from_frame used by BOTH the aggregate and the
   per-node paths, so room-level presence benefits too. (csi.rs carries the identical
   change in its dead mirror copy to keep the two in sync.)

2. parse_esp32_frame header offsets were 2 bytes early vs the firmware layout
   (csi_collector.c csi_serialize_frame: seq @ [12..16), rssi @ [16], noise_floor @
   [17]). rssi was decoded from sequence-counter byte 2 — which stays 0 for the first
   65,536 frames — yielding an impossible rssi=0 dBm that zeroed the RSSI fusion
   weights and the SNR-based signal_quality. The I/Q payload at byte 20 was already
   correct (CSI_HEADER_SIZE == 20), so amplitude-derived features were unaffected.

Adds regression tests asserting motion_score is amplitude-scale-invariant and that a
quiet high-amplitude signal does not saturate. Full binary suite green (103 tests).

Validated live on the 2-node mesh: RSSI now reports real values (-28..-74 dBm, was 0)
and an empty room now produces genuine low-motion frames. A residual over-read remains
(real multi-subcarrier CSI reads elevated even when empty) — that intrinsic empty-vs-
still ambiguity needs a learned reference (adaptive classifier retrain), tracked separately.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-03 08:02:57 -06:00
..
benches ADR-115: Home Assistant + Matter integration (#778) 2026-05-23 16:13:28 -04:00
examples fix(sensing-server): wire MQTT publisher into the binary — closes #872 2026-05-31 09:39:21 -04:00
src Fix antenna-regime presence over-read: de-saturate motion_score + correct CSI header offsets 2026-06-03 08:02:57 -06:00
tests test(mqtt): drive per-node snapshots in discovery integration tests — #898 2026-06-02 10:29:17 +02:00
Cargo.toml chore(cogs): publish cog-ha-matter 0.3.0 + bump signal/sensing-server to 0.3.1 2026-05-25 11:01:46 -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