wifi-densepose/plugins/ruview/codex/prompts/ruview-app.md

/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-server against 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-vitals crate (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-mat crate, 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}/.