GOAP-based planning system for dynamically prioritizing which ADRs to
implement next based on current project state, available hardware, user
goals, and resource constraints.
Key design decisions:
- 25 boolean feature flags + 5 hardware flags + 6 quality metrics
- ~80 actions mapped to ADR implementation phases
- Sublinear search via backward relevance pruning, hierarchical tier
decomposition, incremental replanning, and admissible heuristics
- PageRank-based priority when no specific goal is given
- Integration with claude-flow swarm for dispatching to agents
Co-Authored-By: claude-flow <ruv@ruv.net>
Replace dead URLs for MM-Fi and Wi-Pose datasets with working links:
- MM-Fi: https://ntu-aiot-lab.github.io/mm-fi + GitHub repo with download links
- Wi-Pose: https://github.com/NjtechCVLab/Wi-PoseDataset with Google Drive links
Also corrects Wi-Pose source attribution (Entropy 2023, 12 subjects).
Fixes#84
Co-Authored-By: claude-flow <ruv@ruv.net>
Four-phase approach: eigenvalue-based person count estimation, NMF signal
decomposition, multi-skeleton generation with Kalman tracking, and neural
multi-person model training via RVF pipeline.
Ref: #97
Co-Authored-By: claude-flow <ruv@ruv.net>
* 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>
- Heatmap: Gaussian radial blobs per keypoint with per-person hue,
faint skeleton overlay at 25% opacity
- Dense: body region segmentation with colored filled polygons for
head, torso, arms, legs — thick strokes + joint circles
- Keypoints: now also renders bounding box and confidence
- Previously both heatmap and dense were stubs falling back to skeleton
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>
Reflects current state: Rust sensing server as primary backend,
sensing tab with 3D signal field, data source indicators, estimation
mode badge, setup guide, Docker deployment with CSI_SOURCE, and
updated file tree with all components/services.
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>
- Added IosRssiService to handle synthetic RSSI data for iOS.
- Created WebRssiService to simulate RSSI scanning on the web.
- Defined shared types for WifiNetwork and RssiService in rssi.service.ts.
- Introduced simulation service to generate synthetic sensing data.
- Implemented WebSocket service for real-time data handling with reconnection logic.
- Established Zustand stores for managing application state related to MAT and pose data.
- Developed theme context and utility functions for consistent styling and formatting.
- Added type definitions for various application entities including API responses and sensing data.
- Created utility functions for color mapping and URL validation.
- Configured TypeScript settings for the mobile application.
- Deleted Android and iOS RSSI service implementations.
- Removed simulation service and its associated data generation logic.
- Eliminated related types and stores for managing RSSI and simulation data.
- Cleaned up theme context and utility functions related to color and formatting.
- Removed unused type definitions and configuration files.
Windows treats CLAUDE.md and claude.md as the same file. Remove the
lowercase variant to prevent merge conflicts on case-insensitive systems.
Co-Authored-By: claude-flow <ruv@ruv.net>
On case-insensitive Windows both files map to the same physical file but
Git tracks them as separate index entries. Force-update CLAUDE.md to match.
Co-Authored-By: claude-flow <ruv@ruv.net>
Synchronize project instructions to reflect the full RuvSense (ADR-029/030),
RuView (ADR-031), and security hardening (ADR-032) work now present on this
branch. Adds comprehensive crate and module reference tables, updated
workspace test commands, and witness verification instructions.
Co-Authored-By: claude-flow <ruv@ruv.net>
Fix 4 test failures in the ADR-030 exotic sensing tier modules:
- field_model::test_perturbation_extraction: Use 8 subcarriers with 2
modes and varied calibration data so perturbation on subcarrier 5
(not captured by any environmental mode) remains visible in residual.
- longitudinal::test_drift_detected_after_sustained_deviation: Use 30
baseline days with tiny noise to anchor Welford stats, then inject
deviation of 5.0 (vs 0.1 baseline) so z-score exceeds 2.0 even as
drifted values are accumulated into the running statistics.
- longitudinal::test_monitoring_level_escalation: Same strategy with 30
baseline days and deviation of 10.0 to sustain z > 2.0 for 7+ days,
reaching RiskCorrelation monitoring level.
- tomography::test_nonzero_attenuation_produces_density: Fix ISTA solver
oscillation by replacing max-column-norm Lipschitz estimate with
Frobenius norm squared upper bound, ensuring convergent step size.
Also use stronger attenuations (5.0-16.0) and lower lambda (0.001).
All 209 ruvsense tests now pass. Workspace compiles cleanly.
Co-Authored-By: claude-flow <ruv@ruv.net>
The make_noisy_kpts test helper used noise=0.1 with GT coordinates
spread across [0, 0.85], producing a large bbox diagonal that made
even noisy predictions fall within PCK@0.2 threshold. Reduce GT
coordinate range and increase noise to 0.5 so the test correctly
verifies that noisy predictions produce PCK < 1.0.
Co-Authored-By: claude-flow <ruv@ruv.net>
- tomography.rs: use checked_mul for nx*ny*nz to prevent integer overflow
on adversarial grid configurations
- phase_align.rs: add defensive bounds check in mean_phase_on_indices to
prevent panic on out-of-range subcarrier indices
- multistatic.rs: stabilize softmax in attention_weighted_fusion with
max-subtraction to prevent exp() overflow on extreme similarity values
Co-Authored-By: claude-flow <ruv@ruv.net>
- New collapsed section before Installation linking to witness log,
ADR-028, and bundle generator
- Shows test counts, proof hash, and 3-command verification steps
Co-Authored-By: claude-flow <ruv@ruv.net>
ruv
harshit
Yossi Elkrief
Claude