Add environment-tuned activity classification that learns from labeled
ESP32 CSI recordings, replacing brittle static thresholds.
- Adaptive classifier: 15-feature logistic regression trained from JSONL
recordings (variance, motion band, subcarrier stats: skew, kurtosis,
entropy, IQR). Trains in <1s, persists as JSON, auto-loads on restart.
- Three-stage signal smoothing: adaptive baseline subtraction (α=0.003),
EMA + trimmed-mean median filter (21-frame window), hysteresis debounce
(4 frames). Motion classification now stable across seconds, not frames.
- Vital signs stabilization: outlier rejection (±8 BPM HR, ±2 BPM BR),
trimmed mean, dead-band (±2 BPM HR), EMA α=0.02. HR holds steady for
10+ seconds instead of jumping 50 BPM every frame.
- Observatory auto-detect: always probes /health on startup, connects
WebSocket to live ESP32 data automatically.
- New API endpoints: POST /api/v1/adaptive/train, GET /adaptive/status,
POST /adaptive/unload for runtime model management.
- Updated user guide with Observatory, adaptive classifier tutorial,
signal smoothing docs, and new troubleshooting entries.
The web UI had persistent 404 errors on model, recording, and training
endpoints, and the sensing WebSocket never connected on Dashboard/Live
Demo tabs because sensingService.start() was only called lazily on
Sensing tab visit.
Server (main.rs):
- Add 14 fully-functional Axum handlers: model CRUD (7), recording
lifecycle (4), training control (3)
- Scan data/models/ and data/recordings/ at startup
- Recording writes CSI frames to .jsonl via tokio background task
- Model load/unload lifecycle with state tracking
Web UI (app.js):
- Import and start sensingService early in initializeServices() so
Dashboard and Live Demo tabs connect to /ws/sensing immediately
Mobile (ws.service.ts):
- Fix WebSocket URL builder to use same-origin port instead of
hardcoded port 3001
Mobile (jest.config.js):
- Fix testPathIgnorePatterns that was ignoring the entire test directory
Mobile (25 test files):
- Replace all it.todo() placeholder tests with real implementations
covering components, services, stores, hooks, screens, and utils
ADR-043 documents all changes.
* 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>
- Docker default changed from --source simulated to --source auto
(auto-detects ESP32 on UDP 5005, falls back to simulation)
- Pose derivation now driven by real sensing features: motion_band_power,
breathing_band_power, variance, dominant_freq_hz, change_points
- Temporal feature extraction: 100-frame circular buffer, Goertzel
breathing rate estimation (0.1-0.5 Hz), frame-to-frame L2 motion
detection, SNR-based signal quality metric
- Signal field driven by subcarrier variance spatial mapping instead
of fixed animation circle
- UI data source indicators: LIVE/RECONNECTING/SIMULATED banner on
sensing tab, estimation mode badge on live demo tab
- Setup guide panel explaining ESP32 count requirements for each
capability level (1x: presence, 3x: localization, 4x+: full pose)
- Tick rate improved from 500ms to 100ms (2fps to 10fps)
- Fixed Option<f64> division bug from PR #83
- ADR-035 documents all decisions
Closes#86
Co-Authored-By: claude-flow <ruv@ruv.net>
The UI had hardcoded localhost:8080 for HTTP and localhost:8765 for
WebSocket, causing "Backend unavailable" when served from Docker
(port 3000) or any non-default port.
Changes:
- api.config.js: BASE_URL now uses window.location.origin instead
of hardcoded localhost:8080
- api.config.js: buildWsUrl() uses window.location.host instead of
hardcoded localhost:8080
- sensing.service.js: WebSocket URL derived from page origin instead
of hardcoded localhost:8765
- main.rs: Added /ws/sensing route to the HTTP server so WebSocket
and REST are reachable on a single port
Fixes#55
Co-Authored-By: claude-flow <ruv@ruv.net>
- Snapshot best-epoch weights during training and restore before
checkpoint/RVF export (prevents exporting overfit final-epoch params)
- Add CsiToPoseTransformer::zeros() for fast zero-init when weights
will be overwritten, avoiding wasteful Xavier init during gradient
estimation (~2*param_count transformer constructions per batch)
- Deduplicate synthetic data generation in main.rs training mode
Co-Authored-By: claude-flow <ruv@ruv.net>
- Add docker/ folder with Dockerfile.rust (132MB), Dockerfile.python (569MB),
and docker-compose.yml
- Remove stale root-level Dockerfile and docker-compose files
- Implement --export-rvf CLI flag for standalone RVF package generation
- Generate wifi-densepose-v1.rvf (13KB) with model weights, vital config,
SONA profile, and training provenance
- Update README with Docker pull/run commands and RVF export instructions
- Update test count to 542+ and fix Docker port mappings
- Reply to issues #43, #44, #45 with Docker/RVF availability
Co-Authored-By: claude-flow <ruv@ruv.net>
Replace Python FastAPI + WebSocket servers with a single 2.1MB Rust binary
(wifi-densepose-sensing-server) that serves all UI endpoints:
- REST: /health/*, /api/v1/info, /api/v1/pose/current, /api/v1/pose/stats,
/api/v1/pose/zones/summary, /api/v1/stream/status
- WebSocket: /api/v1/stream/pose (pose_data with 17 COCO keypoints),
/ws/sensing (raw sensing_update stream on port 8765)
- Static: /ui/* with no-cache headers
WiFi-derived pose estimation: derive_pose_from_sensing() generates 17 COCO
keypoints from CSI/WiFi signal data with motion-driven animation.
Data sources: ESP32 CSI via UDP :5005, Windows WiFi via netsh, simulation
fallback. Auto-detection probes each in order.
UI changes:
- Point all endpoints to Rust server on :8080 (was Python :8000)
- Fix WebSocket sensing URL to include /ws/sensing path
- Remove sensingOnlyMode gating — all tabs init normally
- Remove api.service.js sensing-only short-circuit
- Fix clearPingInterval bug in websocket.service.js
Also removes obsolete k8s/ template manifests.
Co-Authored-By: claude-flow <ruv@ruv.net>
ruv