wifi-densepose/v2/crates/wifi-densepose-sensing-server
jhancuff 4f97bd33ab fix(sensing-server): wire training_api::routes() into HTTP router
The proper supervised training pipeline in training_api.rs (with
real_training_loop using analytical gradients on a regularised
linear model + RecordedFrame deserialisation + .rvf export) was
implemented but never wired into the http_app router.

main.rs registered stub handlers at /api/v1/train/{start,status,stop}
that just flipped a String field. POST /train/start returned
"running" without invoking any training; GET /train/status returned
the stub string instead of a TrainingStatus struct.

This change:
  - Declares `mod training_api;` in main.rs so its routes are reachable
  - Replaces AppStateInner's `training_status: String` and
    `training_config: Option<serde_json::Value>` with the proper
    `training_state: training_api::TrainingState` and
    `training_progress_tx: broadcast::Sender<String>` fields the
    real handlers require
  - Removes the three stub handlers (train_status/train_start/
    train_stop) and their .route() registrations
  - Adds `.merge(training_api::routes())` to expose the real pipeline
    plus /ws/train/progress, /train/pretrain, /train/lora
  - Inlines RECORDINGS_DIR + RecordedFrame into training_api.rs so we
    do not also have to declare `mod recording;` in main.rs
    (recording.rs references AppStateInner::recording_state which is
    not present on the production AppStateInner; pulling it in
    cascades into further refactoring)
  - Switches training_api.rs's `crate::path_safety::safe_id` to
    `wifi_densepose_sensing_server::path_safety::safe_id` because
    path_safety lives in the library tree while training_api lives
    in the binary tree

After this change, POST /api/v1/train/start actually trains, and
GET /api/v1/train/status returns the documented TrainingStatus
struct (active, epoch, total_epochs, train_loss, val_pck, val_oks,
lr, best_pck, best_epoch, patience_remaining, eta_secs, phase).
Verified end-to-end locally on an 80k-frame recording — early-stop
fired at epoch 40, .rvf written to data/models/.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-25 16:20:28 -06:00
..
benches ADR-115: Home Assistant + Matter integration (#778) 2026-05-23 16:13:28 -04:00
examples ADR-115: Home Assistant + Matter integration (#778) 2026-05-23 16:13:28 -04:00
src fix(sensing-server): wire training_api::routes() into HTTP router 2026-05-25 16:20:28 -06:00
tests ADR-115: Home Assistant + Matter integration (#778) 2026-05-23 16:13:28 -04:00
Cargo.toml ADR-115: Home Assistant + Matter integration (#778) 2026-05-23 16:13:28 -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