* feat(server): cross-node RSSI-weighted feature fusion + benchmarks
Adds fuse_multi_node_features() that combines CSI features across all
active ESP32 nodes using RSSI-based weighting (closer node = higher weight).
Benchmark results (2 ESP32 nodes, 30s, ~1500 frames):
Metric | Baseline | Fusion | Improvement
---------------------|----------|---------|------------
Variance mean | 109.4 | 77.6 | -29% noise
Variance std | 154.1 | 105.4 | -32% stability
Confidence | 0.643 | 0.686 | +7%
Keypoint spread std | 4.5 | 1.3 | -72% jitter
Presence ratio | 93.4% | 94.6% | +1.3pp
Person count still fluctuates near threshold — tracked as known issue.
Verified on real hardware: COM6 (node 1) + COM9 (node 2) on ruv.net.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(ui): add client-side lerp smoothing to pose renderer
Keypoints now interpolate between frames (alpha=0.25) instead of
jumping directly to new positions. This eliminates visual jitter
that persists even with server-side EMA smoothing, because the
renderer was drawing every WebSocket frame at full rate.
Applied to skeleton, keypoints, and dense body rendering paths.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat: DynamicMinCut person separation + UI lerp smoothing
- Added ruvector-mincut dependency to sensing server
- Replaced variance-based person scoring with actual graph min-cut on
subcarrier temporal correlation matrix (Pearson correlation edges,
DynamicMinCut exact max-flow)
- Recalibrated feature scaling for real ESP32 data ranges
- UI: client-side lerp interpolation (alpha=0.25) on keypoint positions
- Dampened procedural animation (noise, stride, extremity jitter)
- Person count thresholds retuned for mincut ratio
Co-Authored-By: claude-flow <ruv@ruv.net>
* docs: update CHANGELOG with v0.5.1-v0.5.3 releases
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