wifi-densepose/v2/crates/wifi-densepose-sensing-server
arsen da4c123df9 feat(adr-120): windowed temporal classifier (W-MLP) — 53.53% → 90.40%
Adds WindowedMlpModel: 440 → 64 ReLU → n_classes, stacks last 20
frames × 22 features as input. Captures temporal patterns that
frame-level classifiers physically cannot see (walking cadence,
sit-stand cycles, gesture rhythm).

AppStateInner gets feature_window: VecDeque<[f64; 22]> (cap 20)
auto-pushed at the 3 tick sites before adaptive_override. The
classify_window API flattens the buffer (oldest first) + current
frame's features → 440-d input → softmax over classes. Cold-start
(<20 frames) falls back to frame-level MLP.

AdaptiveModel now carries all three classifiers side-by-side:
LogReg (ADR-118), MLP (ADR-119), W-MLP (this). classify_window
picks W-MLP first; legacy classify() picks MLP > LogReg.

Result on the same 6-node, 7-class, 151,329-frame dataset:
  LogReg:   49.58%
  MLP:      53.53%
  W-MLP:    90.40%  (+36.87 pts over MLP, +50.0 pts over original
                     2-node 15-feature LogReg baseline)

Per-class W-MLP accuracy:
  absent          100% (was 41%)
  present_still   100% (was 99%, saturated)
  transition       86% (was 36%)  — sit/stand cadence captured
  waving           90% (was 38%)  — gesture cadence captured
  present_moving   82% (was 33%)  — walking step cadence captured
  active           74% (was 30%)  — jumping bursts captured

Loss broke through frame-level plateau (1.15 → 0.25). Caveat:
90.4% is training-set accuracy; ~28k weights on ~30k windowed
samples means some overfitting likely. Held-out test set
recommended as follow-up.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-18 01:02:38 +07:00
..
src feat(adr-120): windowed temporal classifier (W-MLP) — 53.53% → 90.40% 2026-05-18 01:02:38 +07:00
static feat(adr-107): progress bar in raw.html calibrate button 2026-05-17 16:34:14 +07:00
tests feat(adr-117): process hygiene + pose path honesty + audit sweep 2026-05-17 19:24:04 +07:00
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