2 Commits

Author	SHA1	Message	Date
ruv	33f5abd0e0	feat: ruvector + DynamicMinCut optimizations for WiFlow training (#362) ... Add 4 ruvector-inspired optimizations to the training pipeline: - O6: Subcarrier selection (ruvector-solver) — variance-based top-K selection reduces 128→56 subcarriers (56% input reduction) - O7: Attention-weighted subcarriers (ruvector-attention) — motion- correlated weighting amplifies informative channels - O8: Stoer-Wagner min-cut person separation (ruvector-mincut) — identifies person-specific subcarrier clusters via correlation graph partitioning for multi-person training - O9: Multi-SPSA gradient estimation — K=3 perturbations per step reduces gradient variance by sqrt(3) vs single SPSA Also fixes data loader to accept both `kp`/`keypoints` field names and flat CSI arrays with `csi_shape`, and scalar `conf` values. Co-Authored-By: claude-flow <ruv@ruv.net>	2026-04-06 14:22:08 -04:00
ruv	e3522ddcda	feat: camera ground-truth training pipeline (ADR-079, #362) ... Add 4 scripts for camera-supervised WiFlow pose training: - collect-ground-truth.py: synchronized webcam + CSI capture via MediaPipe PoseLandmarker (17 COCO keypoints at 30fps) - align-ground-truth.js: time-align camera keypoints with CSI windows using binary search, confidence-weighted averaging - train-wiflow-supervised.js: 3-phase supervised training (contrastive pretrain → supervised keypoint regression → bone-constrained refinement) with curriculum learning and CSI augmentation - eval-wiflow.js: PCK@10/20/50, MPJPE, per-joint breakdown, baseline proxy mode for benchmarking Baseline benchmark (proxy poses, no camera supervision): PCK@10: 11.8% | PCK@20: 35.3% | PCK@50: 94.1% | MPJPE: 0.067 Camera pipeline validated over Tailscale to Mac Mini M4 Pro (1920x1080, 14/17 keypoints visible, MediaPipe confidence 0.94-1.0). Target after camera-supervised training: PCK@20 > 50% Closes #362 Co-Authored-By: claude-flow <ruv@ruv.net>	2026-04-06 14:07:25 -04:00