* feat: RVF training pipeline & UI integration (ADR-036) Implement full model training, management, and inference pipeline: Backend (Rust): - recording.rs: CSI recording API (start/stop/list/download/delete) - model_manager.rs: RVF model loading, LoRA profile switching, model library - training_api.rs: Training API with WebSocket progress streaming, simulated training mode with realistic loss curves, auto-RVF export on completion - main.rs: Wire new modules, recording hooks in all CSI paths, data dirs UI (new components): - ModelPanel.js: Dark-mode model library with load/unload, LoRA dropdown - TrainingPanel.js: Recording controls, training config, live Canvas charts - model.service.js: Model REST API client with events - training.service.js: Training + recording API client with WebSocket progress UI (enhancements): - LiveDemoTab: Model selector, LoRA profile switcher, A/B split view toggle, training quick-panel with 60s recording shortcut - SettingsPanel: Full dark mode conversion (issue #92), model configuration (device, threads, auto-load), training configuration (epochs, LR, patience) - PoseDetectionCanvas: 10-frame pose trail with ghost keypoints and motion trajectory lines, cyan trail toggle button - pose.service.js: Model-inference confidence thresholds UI (plumbing): - index.html: Training tab (8th tab) - app.js: Panel initialization and tab routing - style.css: ~250 lines of training/model panel dark-mode styles 191 Rust tests pass, 0 failures. Closes #92. Refs: ADR-036, #93 Co-Authored-By: claude-flow <ruv@ruv.net> * fix: real RuVector training pipeline + UI service fixes Training pipeline (training_api.rs): - Replace simulated training with real signal-based training loop - Load actual CSI data from .csi.jsonl recordings or live frame history - Extract 180 features per frame: subcarrier amplitudes, temporal variance, Goertzel frequency analysis (9 bands), motion gradients, global stats - Train calibrated linear CSI-to-pose mapping via mini-batch gradient descent with L2 regularization (ridge regression), Xavier init, cosine LR decay - Self-supervised: teacher targets from derive_pose_from_sensing() heuristics - Real validation metrics: MSE and PCK@0.2 on 80/20 train/val split - Export trained .rvf with real weights, feature normalization stats, witness - Add infer_pose_from_model() for live inference from trained model - 16 new tests covering features, training, inference, serialization UI fixes: - Fix double-URL bug in model.service.js and training.service.js (buildApiUrl was called twice — once in service, once in apiService) - Fix route paths to match Rust backend (/api/v1/train/*, /api/v1/recording/*) - Fix request body formats (session_name, nested config object) - Fix top-level await in LiveDemoTab.js blocking module graph - Dynamic imports for ModelPanel/TrainingPanel in app.js - Center nav tabs with flex-wrap for 8-tab layout Co-Authored-By: claude-flow <ruv@ruv.net> * fix: WebSocket onOpen race condition, data source indicators, auto-start pose detection - Fix WebSocket onOpen race condition in websocket.service.js where setupEventHandlers replaced onopen after socket was already open, preventing pose service from receiving connection signal - Add 4-state data source indicator (LIVE/SIMULATED/RECONNECTING/OFFLINE) across Dashboard, Sensing, and Live Demo tabs via sensing.service.js - Add hot-plug ESP32 auto-detection in sensing server (auto mode runs both UDP listener and simulation, switches on ESP32_TIMEOUT) - Auto-start pose detection when backend is reachable - Hide duplicate PoseDetectionCanvas controls when enableControls=false - Add standalone Demo button in LiveDemoTab for offline animated demo - Add data source banner and status styling Co-Authored-By: claude-flow <ruv@ruv.net> |
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README.md
wifi-densepose-sensing-server
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
CsiFramerepresentation. - 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
.rvffile 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
netshintegration for BSSID discovery viawifi-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
Related Crates
| 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