- Add v0.7.0 section with 92.9% PCK@20 result and new scripts
- Add camera-supervised training section to user guide with step-by-step
- Update release table (v0.7.0 as latest)
- Update ADR count (62 → 79)
- Update beta notice with camera ground-truth link
Co-Authored-By: claude-flow <ruv@ruv.net>
- Add activation clamping [-10, 10] in TCN forward pass to prevent NaN
from real CSI amplitude ranges after normalization
- Add safe sigmoid with input clamping [-20, 20]
- Add scripts/record-csi-udp.py: lightweight ESP32 CSI UDP recorder
Validated on real paired data (345 samples):
ESP32 CSI: 7,000 frames at 23fps from COM8
Mac camera: 6,470 frames at 22fps via MediaPipe
PCK@20: 92.8% | Eval loss: 0.083 | Bone loss: 0.008
Co-Authored-By: claude-flow <ruv@ruv.net>
Add --scale flag with 4 presets for dataset-appropriate sizing:
lite: ~190K params, 2 TCN blocks k=3 (trains in seconds)
small: ~200K params, 4 TCN blocks k=5 (trains in minutes)
medium: ~800K params, 4 TCN blocks k=7 (trains in ~15 min)
full: ~7.7M params, 4 TCN blocks k=7 (trains in hours)
Refactored model to use dynamic TCN block count, kernel size,
channel widths, hidden dim, and SPSA perturbation count — all
driven by the scale preset. Default is 'lite' for fast iteration.
Validated: lite model completes 30 epochs on 265 samples in ~2 min
on Windows CPU (vs stuck at epoch 1 with full model).
Scale up with: --scale small|medium|full as dataset grows.
Co-Authored-By: claude-flow <ruv@ruv.net>
- ADR-079: strip SSH user/IP from optimization description
- mac-mini-train.sh: replace hardcoded IP with env var WINDOWS_HOST
Co-Authored-By: claude-flow <ruv@ruv.net>
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>
- Add version.txt (0.6.0) read by CMakeLists.txt so
esp_app_get_description()->version matches the release tag
- Log firmware version on boot: "v0.6.0 — Node ID: X"
- Remove stale Kconfig help text (said default 2.0, actual is 15.0)
Fixes the version mismatch reported in #354 where flashing v0.5.3
binaries showed v0.4.3 because PROJECT_VER was never set.
Co-Authored-By: claude-flow <ruv@ruv.net>
JSON.stringify fails on 1M+ triplets. Training succeeded (33.3%
improvement) but export crashed. Now skips export when >100K triplets.
Co-Authored-By: claude-flow <ruv@ruv.net>
Windows firewall blocks UDP on 0.0.0.0 — must bind to specific WiFi IP.
- seed_csi_bridge.py: --bind-addr auto (auto-detects WiFi IP)
- rf-scan.js: --bind <ip> option (default 0.0.0.0, use 192.168.1.x on Windows)
Confirmed: 195 frames received from both ESP32 nodes with --bind 192.168.1.20
Co-Authored-By: claude-flow <ruv@ruv.net>
Option 1: Docker (simulated, no hardware)
Option 2: ESP32 live sensing ($9)
Option 3: Full system with Cognitum Seed ($140)
Also shows RF scan, SNN, and MinCut commands for v0.5.5 capabilities.
Co-Authored-By: claude-flow <ruv@ruv.net>
Replace dry metric table with human-readable results that explain
why each number matters. 14 benchmarks with real-world significance.
Co-Authored-By: claude-flow <ruv@ruv.net>
Stoer-Wagner min-cut on subcarrier correlation graph replaces broken
threshold-based person counting (was always 4, now correct).
Validated: 24/24 windows correctly report 1 person on test data
where old firmware reported 4. Pure JS, <5ms per window.
- mincut-person-counter.js: live UDP + JSONL replay, overrides vitals
- csi-graph-visualizer.js: ASCII spectrum + correlation heatmap
- ADR-075: algorithm, comparison, migration path
Co-Authored-By: claude-flow <ruv@ruv.net>
128→64→8 SNN with STDP online learning — adapts to room in <30s
without labels. Event-driven: 16-160x less compute than FC encoder.
- snn-csi-processor.js: live UDP with ASCII visualization, EWMA
- ADR-073 updated with SNN integration for multi-channel fusion
- Fixed magic number parsing to use ADR-018 format (0xC5110001)
Co-Authored-By: claude-flow <ruv@ruv.net>
Contains GCloud project ID and secret names — not appropriate for
a public repo. Publishing instructions kept in scripts/ only.
Co-Authored-By: claude-flow <ruv@ruv.net>
Clone, copy data via Tailscale, train, benchmark, sync results,
publish to HuggingFace — all automated for M4 Pro hardware.
Co-Authored-By: claude-flow <ruv@ruv.net>
- publish-huggingface.sh: retrieves HF token from GCloud Secrets,
uploads models to ruvnet/wifi-densepose-pretrained
- publish-huggingface.py: Python alternative with --dry-run support
- docs/huggingface/MODEL_CARD.md: beginner-friendly model card with
WiFi sensing explanation, quick start code, hardware BOM, and citation
GCloud Secret: HUGGINGFACE_API_KEY in project cognitum-20260110
Co-Authored-By: claude-flow <ruv@ruv.net>
- #249 (multi-node person counting) fixed by ADR-068 in v0.5.3
- #318 (training plateau) resolved
- Add #348 (n_persons overcount) as current known issue
- Add Cognitum Seed link for spatial resolution improvement
Co-Authored-By: claude-flow <ruv@ruv.net>
ruv