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>
* 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>
* chore: update vendored ruvector to latest main (v2.1.0-40)
Was at v2.0.5-172 (f8f2c600a), now at v2.1.0-40 (050c3fe6f).
316 commits with new crates: ruvector-coherence, sona, ruvector-core,
ruvector-gnn improvements, and security hardening.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat: RuVector Phases 2+3 — temporal smoothing, kinematic constraints, coherence gating
Phase 2 (sensing server):
- Temporal keypoint smoothing via EMA (alpha=0.3) with coherence-adaptive blending
- Coherence scoring: running variance of motion_energy over 20 frames
- Low coherence → reduce alpha to 0.1 (trust measurements less)
- Per-node prev_keypoints for frame-to-frame smoothing
- Bone length clamping (±20%) in derive_single_person_pose
Phase 3 (signal crate):
- SkeletonConstraints: Jakobsen relaxation (3 iterations) on 12-bone
COCO-17 kinematic tree — prevents impossible skeletons
- CompressedPoseHistory: two-tier storage (hot f32 + warm i16 quantized)
for trajectory matching and re-ID
- 8 new tests for constraints + history
Vendored ruvector updated to v2.1.0-40 (latest main, 316 commits).
Workspace deps remain at v2.0.4 (crates.io) until v2.1.0 is published.
647 tests pass across both crates (0 failures).
Refs #296
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(server): use max instead of sum for multi-node person aggregation
With nodes in the same room, each node sees the same people. Summing
per-node counts double-counted (2 nodes × 1 person = 2 persons).
Now uses max() so 2 nodes × 1 person = 1 person.
Verified on real hardware: COM6 (node 1) + COM9 (node 2) on ruv.net,
estimated_persons=1 with 1 person in room.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(server): reduce skeleton jitter + raise person count thresholds
- EMA alpha 0.3→0.15, low-coherence 0.1→0.05
- Remove tick-based noise (main jitter source)
- Breathing 5x slower, extremity jitter 3x smaller, stride 2x smaller
- Person count 1→2 threshold 0.65→0.80
- Aggregation sum→max for same-room nodes
Verified on COM6+COM9: 1 person stable.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(signal): subcarrier importance weighting via mincut partition (Phase 1)
Adds subcarrier_importance_weights() to ruvector signal crate — converts
mincut partition into per-subcarrier float weights (>1.0 for sensitive,
0.5 for insensitive subcarriers).
Sensing server now uses weighted mean/variance in extract_features_from_frame
instead of treating all 56 subcarriers equally. This emphasizes body-motion-
sensitive subcarriers and reduces noise from static multipath.
Expected: ~26% reduction in keypoint jitter (±15cm → ±11cm RMS).
284 tests pass (191 trainer + 51 lib + 18 vital_signs + 16 dataset + 8 multi_node).
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(firmware): stack overflow risk + tick-rate independence (review findings)
Critical fixes from deep review:
1. **Stack overflow prevention**: Moved BPM scratch buffers (br_buf, hr_buf)
from stack to static storage in both process_frame() and
update_multi_person_vitals(). Combined stack was ~6.5-7.5 KB of 8 KB
limit — now reduced by ~4 KB to safe margins.
2. **Tick-rate independence**: Post-batch yield now uses
pdMS_TO_TICKS(20) with min-1 guard instead of raw vTaskDelay(2).
Previously assumed 100Hz tick rate.
3. **EDGE_BATCH_LIMIT to header**: Moved from local const to
edge_processing.h #define for configurability.
Firmware builds clean at 843 KB.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(server): stale node eviction, remove unsafe pointer (review findings)
Critical fixes from deep review:
1. **Stale node eviction**: node_states HashMap now evicts nodes with no
frame for >60 seconds, every 100 ticks. Prevents unbounded memory
growth and stale smoothing data when nodes are replaced.
2. **Remove unsafe raw pointer**: Replaced the unsafe raw pointer to
adaptive_model (used to break borrow checker deadlock with
node_states) with a safe .clone() before the mutable borrow.
AdaptiveModel derives Clone so this is a clean copy.
284 tests pass, zero failures.
Co-Authored-By: claude-flow <ruv@ruv.net>
The server parsed rssi from buf[14] and noise_floor from buf[15], but
the firmware (csi_collector.c) packs them at buf[16] and buf[17]:
Firmware: n_subcarriers=u16(6-7) freq=u32(8-11) seq=u32(12-15) rssi=i8(16)
Server: n_subcarriers=u8(6) freq=u16(8-9) seq=u32(10-13) rssi=i8(14) ← WRONG
This caused RSSI to read the high byte of the sequence counter instead
of the actual signed RSSI value, producing positive values (e.g., +9)
instead of the correct negative values (e.g., -46 dBm).
Added inline documentation of the frame layout matching csi_collector.c.
Closes#332
- Container: espressif/idf:v5.2 → v5.4 (matches QEMU workflow)
- Replace xxd calls with od (xxd not available in IDF container)
- Add ota_data_initial.bin to artifact upload
- Extend artifact retention to 90 days
The xxd:not-found error was blocking all Firmware CI builds since the
container migration. This unblocks binary artifact generation for
release assets.
Closes#327
Co-Authored-By: claude-flow <ruv@ruv.net>
* docs(adr): ADR-068 per-node state pipeline for multi-node sensing (#249)
Documents the architectural change from single shared state to per-node
HashMap<u8, NodeState> in the sensing server. Includes scaling analysis
(256 nodes < 13 MB), QEMU validation plan, and aggregation strategy.
Also links README hero image to the explainer video.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(server): per-node state pipeline for multi-node sensing (ADR-068, #249)
Replaces the single shared state pipeline with per-node HashMap<u8, NodeState>.
Each ESP32 node now gets independent:
- frame_history (temporal analysis)
- smoothed_person_score / prev_person_count
- smoothed_motion / baseline / debounce state
- vital sign detector + smoothing buffers
- RSSI history
Multi-node aggregation:
- Person count = sum of per-node counts for active nodes (seen <10s)
- SensingUpdate.nodes includes all active nodes
- estimated_persons reflects cross-node aggregate
Single-node deployments behave identically (HashMap has one entry).
Simulated data path unchanged for backward compatibility.
Closes#249
Refs #237, #276, #282
Co-Authored-By: claude-flow <ruv@ruv.net>
Documents the architectural change from single shared state to per-node
HashMap<u8, NodeState> in the sensing server. Includes scaling analysis
(256 nodes < 13 MB), QEMU validation plan, and aggregation strategy.
Also links README hero image to the explainer video.
Co-Authored-By: claude-flow <ruv@ruv.net>
List specific known issues (multi-node detection, training plateau,
no pre-trained weights, hardware compatibility) to set expectations
for new users.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(firmware,server): watchdog crash on busy LANs + no detection from edge vitals (#321, #323)
**Firmware (#321):** edge_dsp task now batch-limits frame processing to 4
frames before a 10ms yield. On corporate LANs with high CSI frame rates,
the previous 1-tick-per-frame yield wasn't enough to prevent IDLE1
starvation and task watchdog triggers.
**Sensing server (#323):** When ESP32 runs the edge DSP pipeline (Tier 2+),
it sends vitals packets (magic 0xC5110002) instead of raw CSI frames.
Previously, the server broadcast these as raw edge_vitals but never
generated a sensing_update, so the UI showed "connected" but "0 persons".
Now synthesizes a full sensing_update from vitals data including
classification, person count, and pose generation.
Closes#321Closes#323
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(firmware): address review findings — idle busy-spin and observability
- Fix pdMS_TO_TICKS(5)==0 at 100Hz causing busy-spin in idle path (use
vTaskDelay(1) instead)
- Post-batch yield now 2 ticks (20ms) for genuinely longer pause
- Add s_ring_drops counter to ring_push for diagnosing frame drops
- Expose drop count in periodic vitals log line
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(server): set breathing_band_power for skeleton animation from vitals
When presence is detected via edge vitals, set breathing_band_power to
0.5 so the UI's torso breathing animation works. Previously hardcoded
to 0.0 which made the skeleton appear static even when breathing rate
was being reported.
Co-Authored-By: claude-flow <ruv@ruv.net>
The README Quick Start tells users to `pip install wifi-densepose` and then
`from wifi_densepose import WiFiDensePose`, but no `wifi_densepose` Python
package existed — only `v1/src`. This adds a top-level `wifi_densepose/`
package with a WiFiDensePose facade class matching the documented API, and
updates pyproject.toml to include it in the distribution.
Closes#314
ruv