Run RuView applications for occupancy, breathing and heart rate, activity, pose, sleep, and room changes. Choose suitable examples and startup commands for a simulated demonstration or a working ESP32 setup that captures WiFi signal measurements.
中文简介正在准备中。当前显示英语简介。
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.
安装前可以查看交给 AI 代理的指令内容。
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 | 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) |
# 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
ruview-model-training).docs/wifi-mat-user-guide.md).ruview-advanced-sensing), optionally + Cognitum Seed.examples/environment/ · examples/medical/ · examples/sleep/ · examples/stress/ · examples/happiness-vector/ · examples/ruview_live.py — each has a README.
README.md — feature matrix, latency/throughput numbersdocs/user-guide.md, docs/wifi-mat-user-guide.mdv2/crates/wifi-densepose-signal/src/ruvsense/ (14 modules)还没有评价。欢迎分享你的使用感受。
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还没有评论。来写第一条吧。
Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.
中文简介正在准备中。当前显示英语简介。
Create AgentDB learning plugins that use recorded experience to improve agent decisions. Choose a learning method, store states, actions, and rewards, train models, and evaluate suggested actions through CLI and API workflows.
中文简介正在准备中。当前显示英语简介。
Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.
中文简介正在准备中。当前显示英语简介。
Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.
中文简介正在准备中。当前显示英语简介。
Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.
中文简介正在准备中。当前显示英语简介。
Automate browser tasks with agent-browser, using snapshots that label page elements for the agent. Handle login flows, fill and submit forms, extract page information, and capture screenshots through a repeatable navigation and interaction workflow.
中文简介正在准备中。当前显示英语简介。