Complements #326 (per-node state pipeline) with additional features:
- Dynamic adaptive classifier: discover activity classes from training
data filenames instead of hardcoded array. Users add classes via
filename convention (train_<class>_<desc>.jsonl), no code changes.
- Per-node UI cards: SensingTab shows individual node status with
color-coded markers, RSSI, variance, and classification per node.
- Colored node markers in 3D gaussian splat view (8-color palette).
- Per-node RSSI history tracking in sensing service.
- XSS fix: UI uses createElement/textContent instead of innerHTML.
- RSSI sign fix: ensure dBm values are always negative.
- GET /api/v1/nodes endpoint for per-node health monitoring.
- node_features field in WebSocket SensingUpdate messages.
- Firmware watchdog fix: yield after every frame to prevent IDLE1 starvation.
Addresses #237, #276, #282
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* 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>
- All buttons converted to dark translucent style with colored accents:
Start (green), Stop (red), Reconnect (blue), Demo (purple)
- Header, wrapper, status badge all use dark backgrounds
- Controls in single flat row (no wrapping)
- Mode select dropdown styled for dark theme
- HTML entity icons on all buttons
Co-Authored-By: claude-flow <ruv@ruv.net>
- Convert all Live Demo sidebar panels to dark theme matching rest of UI
- Fix pose_source not reaching LiveDemoTab (was lost in
convertZoneDataToRestFormat — now passes through from WS message)
- Dark backgrounds, muted text, consistent border opacity throughout
- Estimation Mode badge colors adjusted for dark background contrast
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>
- Add Python WebSocket sensing server (ws_server.py) with ESP32 UDP CSI
and Windows RSSI auto-detect collectors on port 8765
- Add Three.js Gaussian splat renderer with custom GLSL shaders for
real-time WiFi signal field visualization (blue→green→red gradient)
- Add SensingTab component with RSSI sparkline, feature meters, and
motion classification badge
- Add sensing.service.js WebSocket client with reconnect and simulation fallback
- Implement sensing-only mode: suppress all DensePose API calls when
FastAPI backend (port 8000) is not running, clean console output
- ADR-019: Document sensing-only UI architecture and data flow
- ADR-020: Migrate AI/model inference to Rust with RuVector ONNX Runtime,
replacing ~2.7GB Python stack with ~50MB static binary
- Add ruvnet/ruvector as upstream remote for RuVector crate ecosystem
Co-Authored-By: claude-flow <ruv@ruv.net>
Add prominent hardware requirements table at top of README documenting
the three paths to real CSI data (ESP32, research NIC, commodity WiFi).
Include remaining Three.js visualization components for dashboard.
https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714
- .github/workflows/verify-pipeline.yml: CI that verifies pipeline
determinism and checks for np.random in production code
- ui/components/body-model.js: Three.js 3D human body model with
24 DensePose body parts mapped to 3D geometry
- v1/requirements-lock.txt: Minimal pinned dependencies for verification
- v1/src/api/dependencies.py: Fix mock auth returns with proper errors
- v1/src/core/router_interface.py: Additional mock mode cleanup
- v1/src/services/pose_service.py: Further mock elimination in service
https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714
Commodity Sensing Module (ADR-013):
- sensing/rssi_collector.py: Real Linux WiFi RSSI collection from
/proc/net/wireless and iw commands, with SimulatedCollector for testing
- sensing/feature_extractor.py: FFT-based spectral analysis, CUSUM
change-point detection, breathing/motion band power extraction
- sensing/classifier.py: Rule-based presence/motion classification
with confidence scoring and multi-receiver agreement
- sensing/backend.py: Common SensingBackend protocol with honest
capability reporting (PRESENCE + MOTION only for commodity)
Proof of Reality Bundle (ADR-011):
- data/proof/generate_reference_signal.py: Deterministic synthetic CSI
with known breathing (0.3 Hz) and walking (1.2 Hz) signals
- data/proof/sample_csi_data.json: Generated reference signal
- data/proof/verify.py: One-command pipeline verification with SHA-256
- data/proof/expected_features.sha256: Expected output hash
Three.js Visualization:
- ui/components/scene.js: 3D scene setup with OrbitControls
Mock Isolation:
- testing/mock_pose_generator.py: Mock pose generation moved out of
production pose_service.py
- services/pose_service.py: Cleaned mock paths
https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714
