wehub-resource-sync
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1.9 KiB
1.9 KiB
/ruview-app — run a RuView sensing application
Run a RuView application. Which one: $ARGUMENTS (one of presence, vitals, pose, sleep, environment, mat, pointcloud, or a novel-RF app name; if empty, show the catalogue and ask).
- presence / vitals / pose / environment →
cd v2 && cargo run -p wifi-densepose-sensing-serveragainst a live ESP32 sink, or the Docker demo (docker run -p 3000:3000 ruvnet/wifi-densepose:latest) for simulated CSI. For environment also-- --model model.rvf --build-index env. Vitals: breathing 6–30 BPM (bandpass 0.1–0.5 Hz), heart rate 40–120 BPM (bandpass 0.8–2.0 Hz),wifi-densepose-vitalscrate (ADR-021). Pose: 17 COCO keypoints via WiFlow (ADR-059 live pipeline) — train for accuracy (/ruview-train). - sleep →
examples/sleep/+node scripts/apnea-detector.js(sleep-stage classification, apnea screening). - mat (Mass Casualty Assessment — disaster survivor detection) →
wifi-densepose-matcrate,docs/wifi-mat-user-guide.md. - pointcloud →
python scripts/mmwave_fusion_bridge.py(camera depth via MiDaS + WiFi CSI + mmWave radar → unified spatial model, ~22 ms, 19K+ pts/frame; ADR-094). - novel RF →
scripts/passive-radar.js,material-classifier.js,device-fingerprint.js,mincut-person-counter.js,gait-analyzer.js(ADR-077/078).
No hardware? Fall back to the Docker demo or python examples/ruview_live.py. Visualisers: node scripts/csi-spectrogram.js, node scripts/csi-graph-visualizer.js.
Help me pick: through-wall → presence/activity (≤5 m depth); stationary subject → vitals/sleep; need skeletons → pose (train it); search & rescue → MAT; best spatial accuracy → 2+ ESP32 nodes + cross-viewpoint fusion (v2/crates/wifi-densepose-ruvector/src/viewpoint/), optionally + Cognitum Seed. Examples: examples/{environment,medical,sleep,stress,happiness-vector}/.