mirror of https://github.com/ruvnet/RuView synced 2026-07-19 16:53:18 +00:00

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ruv 8ff7c2c35a feat(plugins): RuView Claude Code + Codex marketplace plugin

Add `plugins/ruview` — an end-to-end toolkit for working with RuView
(WiFi-DensePose) from Claude Code, mirrored as Codex prompts.

Marketplace: `plugins/.claude-plugin/marketplace.json` (one plugin, `ruview`).

Skills (9): ruview-quickstart, ruview-hardware-setup, ruview-configure,
ruview-applications, ruview-model-training, ruview-advanced-sensing,
ruview-cli-api, ruview-mmwave, ruview-verify — shell-first (cargo / python /
idf.py / docker / node), no claude-flow MCP dependency.

Commands (7): /ruview-start, /ruview-flash, /ruview-provision, /ruview-app,
/ruview-train, /ruview-advanced, /ruview-verify.

Agents (3): ruview-onboarding-guide, ruview-config-engineer,
ruview-training-engineer.

Codex mirror: codex/AGENTS.md + codex/README.md + codex/prompts/*.md (full
command parity, enforced by scripts/smoke.sh).

Docs: docs/adrs/0001-ruview-plugin-contract.md (Proposed). Verification:
scripts/smoke.sh (13 structural checks). Provisioning docs reflect the full
`provision.py` flag set (TDM mesh, edge tiers, vitals, hop channels, Cognitum
Seed, swarm intervals) and the issue #391 NVS-namespace-replace gotcha.

Verified: `claude plugin validate` (plugin + marketplace), loads via
`claude --plugin-dir`, smoke 13/13, and confirmed against an attached ESP32-S3
on COM8 running the RuView CSI firmware (live adaptive_ctrl + csi_collector
serial output).

Co-Authored-By: claude-flow <ruv@ruv.net>

2026-05-11 17:39:16 -04:00

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name, description, allowed-tools

name	description	allowed-tools
ruview-applications	Run RuView sensing applications — presence/occupancy, breathing & heart rate, activity & fall detection, 17-keypoint pose estimation (WiFlow), sleep monitoring & apnea screening, environment mapping, Mass Casualty Assessment (MAT), and the 3D point-cloud fusion demo. Use when someone wants to actually do something with a working RuView setup.	Bash Read Write Edit Glob Grep

RuView Applications

What RuView can sense, and how to run each one. Assumes you have either the Docker demo (simulated CSI) or a live ESP32 sink (see ruview-quickstart / ruview-hardware-setup).

Application catalogue

Application	What it does	Entry point
Presence / occupancy	Detect people through walls, count them, track entries/exits (trained model + PIR fusion, ~0.012 ms latency)	sensing-server live mode; `examples/environment/`
Vital signs	Breathing 6–30 BPM (bandpass 0.1–0.5 Hz), heart rate 40–120 BPM (bandpass 0.8–2.0 Hz), contactless while sleeping/sitting	`wifi-densepose-vitals` crate (ADR-021); `examples/medical/`
Activity recognition	Walking, sitting, gestures, falls — from temporal CSI patterns	RuvSense `gesture.rs` (DTW), `pose_tracker.rs`; `scripts/gait-analyzer.js`
Pose estimation	17 COCO keypoints via WiFlow architecture; dual-modal webcam+WiFi fusion demo	`cargo run -p wifi-densepose-sensing-server` + pose-fusion demo (ADR-059); see `ruview-model-training` to train
Sleep monitoring	Overnight monitoring, sleep-stage classification, apnea screening	`examples/sleep/`; `scripts/apnea-detector.js`
Environment mapping	RF fingerprinting identifies rooms, detects moved furniture, spots new objects	sensing-server `--build-index env`; RuvSense `field_model.rs`, `cross_room.rs`
Mass Casualty Assessment (MAT)	Disaster survivor detection — find people in rubble/smoke	`wifi-densepose-mat` crate; `docs/wifi-mat-user-guide.md`; `examples/medical/`
3D point cloud (optional fusion)	Camera depth (MiDaS) + WiFi CSI + mmWave radar → unified spatial model (~22 ms, 19K+ pts/frame)	`scripts/mmwave_fusion_bridge.py`; ADR-094 (GitHub Pages deploy)
Novel RF apps	Passive radar, material classification, device fingerprinting, mincut person-counting	`scripts/passive-radar.js`, `material-classifier.js`, `device-fingerprint.js`, `mincut-person-counter.js` (ADR-077/078)

Quick recipes

# Docker demo — everything, simulated CSI
docker run -p 3000:3000 ruvnet/wifi-densepose:latest    # http://localhost:3000

# Live sensing server (consumes ESP32 UDP CSI)
cd v2 && cargo run -p wifi-densepose-sensing-server

# Live RF room scan (Cognitum Seed on :5006)
node scripts/rf-scan.js --port 5006
node scripts/snn-csi-processor.js --port 5006

# Embed a trained model + build an environment index
cd v2
cargo run -p wifi-densepose-sensing-server -- --model model.rvf --embed
cargo run -p wifi-densepose-sensing-server -- --model model.rvf --build-index env

# Python live demo
python examples/ruview_live.py

# Spectrogram / graph visualisers
node scripts/csi-spectrogram.js
node scripts/csi-graph-visualizer.js

Picking the right modality

Through a wall, no line of sight → presence + activity; expect ≤5 m depth (Fresnel-zone geometry).
Person stationary (sleeping / sitting) → vitals (breathing first, heart rate needs cleaner signal) + sleep staging.
Need skeletons → pose (WiFlow). Camera-free works but is modest; camera-supervised gets 92.9% PCK@20 — train it (ruview-model-training).
Search & rescue → MAT (docs/wifi-mat-user-guide.md).
"What changed in this room?" → environment mapping / RF fingerprint index.
Best spatial accuracy → 2+ ESP32 nodes + cross-viewpoint fusion (ruview-advanced-sensing), optionally + Cognitum Seed.

Examples directory map

examples/environment/ · examples/medical/ · examples/sleep/ · examples/stress/ · examples/happiness-vector/ · examples/ruview_live.py — each has a README.

Reference

README.md — feature matrix, latency/throughput numbers
docs/user-guide.md, docs/wifi-mat-user-guide.md
ADRs: 021 (vitals), 024 (AETHER contrastive embeddings), 027 (MERIDIAN domain generalization), 041 (edge modules), 059 (live ESP32 pipeline), 077/078 (novel RF apps), 082 (pose tracker output filter), 094 (point cloud)
RuvSense modules: v2/crates/wifi-densepose-signal/src/ruvsense/ (14 modules)

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