* fix(firmware): gate phantom persons + add presence hysteresis (#998, #996) Two ESP32 edge-vitals logic bugs in edge_processing.c. Both are robustness/logic fixes — NOT validated-accuracy claims. True count/PCK vs labelled ground truth remains hardware/data-gated (COM9 ESP32-S3). #998 — n_persons over-counted (reported 4 for one person): update_multi_person_vitals() split top-K subcarriers into top_k_count/2 groups and marked EVERY group active, so one body's multipath always read the full EDGE_MAX_PERSONS. Added two pure, host-testable helpers: - count_distinct_persons(): per-group energy gate (EDGE_PERSON_MIN_ENERGY_RATIO) + spatial dedup (EDGE_PERSON_MIN_SC_SEP) so weak/adjacent multipath groups don't count as separate bodies. Strongest group always counts (>=1). - person_count_debounce(): a gated count must hold EDGE_PERSON_PERSIST_FRAMES consecutive frames before it's emitted, so a single noisy frame can't promote a phantom. The active flags now mark only the strongest stable_count groups. #996 — presence flag flickered at ~50cm despite high presence_score: the bare `score > threshold` compare chattered on a noisy score (field-observed 2.6-26.7 frame-to-frame). Replaced with a Schmitt trigger + clear-debounce (presence_flag_update): assert above threshold, hold in the dead band down to threshold * EDGE_PRESENCE_HYST_RATIO, clear only after EDGE_PRESENCE_CLEAR_FRAMES consecutive sub-low frames. presence_score itself is unchanged and still emitted for consumer-side thresholding. All thresholds are named, documented constants in edge_processing.h. Firmware builds clean for esp32s3 (idf.py build RC=0). Co-Authored-By: claude-flow <ruv@ruv.net> * test(firmware): host C99 tests for vitals count + presence logic (#998, #996) test/test_vitals_count_presence.c pins the two fixes with deterministic host-buildable tests (no ESP-IDF needed). 13 cases / 22 assertions, all passing under gcc 13 -Wall -Wextra: #998 count gate: single strong signature + multipath -> count==1; two well-separated -> 2; two strong-but-adjacent -> 1 (dedup); no signal -> 0; three well-separated -> 3. #998 debounce: transient spike rejected; sustained change accepted; flapping count stays stable. #996 presence: dithering trace -> stable flag (no flicker); brief dips held by clear-debounce; genuine departure clears within hold window; dead-band holds state. The named tuning constants are #include'd from the real edge_processing.h so the test and firmware can never disagree on thresholds. `make run_vitals` / `make host_tests` added; binaries gitignored. Hardware-gated caveat documented in the test header: these pin the decision LOGIC; the exact energy/separation/hysteresis values that best match a real room vs labelled occupancy remain on-device tuning. Co-Authored-By: claude-flow <ruv@ruv.net> * docs: record ESP32 vitals count/presence fixes (#998, #996) CHANGELOG [Unreleased] Fixed: root cause + fix + named constants + test + explicit hardware/data-gated caveat for both bugs. ADR-021 Implementation Notes: dated 2026-06 entry noting the edge-path person-count + presence-flicker fixes are boolean/count emission-logic fixes, not a validated-accuracy claim; thresholds pending on-device calibration. Co-Authored-By: claude-flow <ruv@ruv.net> * fix(sensing-server): emit real field-derived person position/motion to /ws/sensing (#1050) The Observatory 3D figure never animated because the sensing_update WS frame carried no per-person position/motion_score/pose — only image-space keypoints. The FigurePool/PoseSystem (and demo-data.js's own contract) animate each figure from persons[i].position (room-world), .motion_score (0..100), and .pose; none were on the live stream. Honest scope (Case 2): the pipeline has no calibrated per-person room localizer or per-person skeletal pose. New field_localize module extracts the strongest peak(s) from the real signal_field grid (subcarrier variances x motion-band power) and maps the peak cell to Observatory world coords with the exact _buildSignalField transform. motion_score is the measured motion_band_power passed through; pose is set only from a real aggregate posture estimate, else None (never a fabricated skeleton). Empty/below-threshold field -> persons: [] (no phantom); present person with no resolvable peak keeps position [0,0,0], not invented coords. attach_field_positions runs after the tracker step at all five broadcast sites. New position/motion_score/pose fields added to both PersonDetection structs. No UI change needed — the Observatory already reads these fields. Tests: field_localize peak/coordinate/empty/separation units + observatory_persons_field_position_tests (known-peak -> emitted position, empty-room -> no phantom, pose real-or-None, below-threshold honesty). sensing-server bin 441->451, 0 failed. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(changelog): record #1050 Observatory persons position/motion fix Co-Authored-By: claude-flow <ruv@ruv.net> |
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README.md
wifi-densepose-sensing-server
Lightweight Axum server for real-time WiFi sensing with RuVector signal processing.
Overview
wifi-densepose-sensing-server is the operational backend for WiFi-DensePose. It receives raw CSI
frames from ESP32 hardware over UDP, runs them through the RuVector-powered signal processing
pipeline, and broadcasts processed sensing updates to browser clients via WebSocket. A built-in
static file server hosts the sensing UI on the same port.
The crate ships both a library (wifi_densepose_sensing_server) exposing the training and inference
modules, and a binary (sensing-server) that starts the full server stack.
Integrates wifi-densepose-wifiscan for multi-BSSID WiFi scanning per ADR-022 Phase 3.
Features
- UDP CSI ingestion -- Receives ESP32 CSI frames on port 5005 and parses them into the internal
CsiFramerepresentation. - Vital sign detection -- Pure-Rust FFT-based breathing rate (0.1--0.5 Hz) and heart rate (0.67--2.0 Hz) estimation from CSI amplitude time series (ADR-021).
- RVF container -- Standalone binary container format for packaging model weights, metadata, and
configuration into a single
.rvffile with 64-byte aligned segments. - RVF pipeline -- Progressive model loading with streaming segment decoding.
- Graph Transformer -- Cross-attention bottleneck between antenna-space CSI features and the
COCO 17-keypoint body graph, followed by GCN message passing (ADR-023 Phase 2). Pure
std, no ML dependencies. - SONA adaptation -- LoRA + EWC++ online adaptation for environment drift without catastrophic forgetting (ADR-023 Phase 5).
- Contrastive CSI embeddings -- Self-supervised SimCLR-style pretraining with InfoNCE loss, projection head, fingerprint indexing, and cross-modal pose alignment (ADR-024).
- Sparse inference -- Activation profiling, sparse matrix-vector multiply, INT8/FP16 quantization, and a full sparse inference engine for edge deployment (ADR-023 Phase 6).
- Dataset pipeline -- Training dataset loading and batching.
- Multi-BSSID scanning -- Windows
netshintegration for BSSID discovery viawifi-densepose-wifiscan(ADR-022). - WebSocket broadcast -- Real-time sensing updates pushed to all connected clients at
ws://localhost:8765/ws/sensing. - Static file serving -- Hosts the sensing UI on port 8080 with CORS headers.
Modules
| Module | Description |
|---|---|
vital_signs |
Breathing and heart rate extraction via FFT spectral analysis |
rvf_container |
RVF binary format builder and reader |
rvf_pipeline |
Progressive model loading from RVF containers |
graph_transformer |
Graph Transformer + GCN for CSI-to-pose estimation |
trainer |
Training loop orchestration |
dataset |
Training data loading and batching |
sona |
LoRA adapters and EWC++ continual learning |
sparse_inference |
Neuron profiling, sparse matmul, INT8/FP16 quantization |
embedding |
Contrastive CSI embedding model and fingerprint index |
Quick Start
# Build the server
cargo build -p wifi-densepose-sensing-server
# Run with default settings (HTTP :8080, UDP :5005, WS :8765)
cargo run -p wifi-densepose-sensing-server
# Run with custom ports
cargo run -p wifi-densepose-sensing-server -- \
--http-port 9000 \
--udp-port 5005 \
--static-dir ./ui
Using as a library
use wifi_densepose_sensing_server::vital_signs::VitalSignDetector;
// Create a detector with 20 Hz sample rate
let mut detector = VitalSignDetector::new(20.0);
// Feed CSI amplitude samples
for amplitude in csi_amplitudes.iter() {
detector.push_sample(*amplitude);
}
// Extract vital signs
if let Some(vitals) = detector.detect() {
println!("Breathing: {:.1} BPM", vitals.breathing_rate_bpm);
println!("Heart rate: {:.0} BPM", vitals.heart_rate_bpm);
}
Architecture
ESP32 ──UDP:5005──> [ CSI Receiver ]
|
[ Signal Pipeline ]
(vital_signs, graph_transformer, sona)
|
[ WebSocket Broadcast ]
|
Browser <──WS:8765── [ Axum Server :8080 ] ──> Static UI files
Related Crates
| Crate | Role |
|---|---|
wifi-densepose-wifiscan |
Multi-BSSID WiFi scanning (ADR-022) |
wifi-densepose-core |
Shared types and traits |
wifi-densepose-signal |
CSI signal processing algorithms |
wifi-densepose-hardware |
ESP32 hardware interfaces |
wifi-densepose-wasm |
Browser WASM bindings for the sensing UI |
wifi-densepose-train |
Full training pipeline with ruvector |
wifi-densepose-mat |
Disaster detection module |
License
MIT OR Apache-2.0