RuView/ui/README.md

WiFi DensePose UI

A modular, modern web interface for the WiFi DensePose human tracking system. Provides real-time monitoring, WiFi sensing visualization, and pose estimation from CSI (Channel State Information).

Architecture

The UI follows a modular architecture with clear separation of concerns:

ui/
├── app.js                    # Main application entry point
├── index.html                # HTML shell with tab structure
├── style.css                 # Complete CSS design system
├── config/
│   └── api.config.js         # API endpoints and configuration
├── services/
│   ├── api.service.js        # HTTP API client
│   ├── websocket.service.js  # WebSocket connection manager
│   ├── websocket-client.js   # Low-level WebSocket client
│   ├── pose.service.js       # Pose estimation API wrapper
│   ├── sensing.service.js    # WiFi sensing data service (live + simulation fallback)
│   ├── health.service.js     # Health monitoring API wrapper
│   ├── stream.service.js     # Streaming API wrapper
│   └── data-processor.js     # Signal data processing utilities
├── components/
│   ├── TabManager.js         # Tab navigation component
│   ├── DashboardTab.js       # Dashboard with live system metrics
│   ├── SensingTab.js         # WiFi sensing visualization (3D signal field, metrics)
│   ├── LiveDemoTab.js        # Live pose detection with setup guide
│   ├── HardwareTab.js        # Hardware configuration
│   ├── SettingsPanel.js      # Settings panel
│   ├── PoseDetectionCanvas.js # Canvas-based pose skeleton renderer
│   ├── gaussian-splats.js    # 3D Gaussian splat signal field renderer (Three.js)
│   ├── body-model.js         # 3D body model
│   ├── scene.js              # Three.js scene management
│   ├── signal-viz.js         # Signal visualization utilities
│   ├── environment.js        # Environment/room visualization
│   └── dashboard-hud.js      # Dashboard heads-up display
├── utils/
│   ├── backend-detector.js   # Auto-detect backend availability
│   ├── mock-server.js        # Mock server for testing
│   └── pose-renderer.js      # Pose rendering utilities
└── tests/
    ├── test-runner.html       # Test runner UI
    ├── test-runner.js         # Test framework and cases
    └── integration-test.html  # Integration testing page

Features

WiFi Sensing Tab

• 3D Gaussian-splat signal field visualization (Three.js)
• Real-time RSSI, variance, motion band, breathing band metrics
• Presence/motion classification with confidence scores
• Data source banner: green "LIVE - ESP32", yellow "RECONNECTING...", or red "SIMULATED DATA"
• Sparkline RSSI history graph
• "About This Data" card explaining CSI capabilities per sensor count

Live Demo Tab

• WebSocket-based real-time pose skeleton rendering
• Estimation Mode badge: green "Signal-Derived" or blue "Model Inference"
• Setup Guide panel showing what each ESP32 count provides:
  • 1 ESP32: presence, breathing, gross motion
  • 2-3 ESP32s: body localization, motion direction
  • 4+ ESP32s + trained model: individual limb tracking, full pose
• Debug mode with log export
• Zone selection and force-reconnect controls
• Performance metrics sidebar (frames, uptime, errors)

Dashboard

• Live system health monitoring
• Real-time pose detection statistics
• Zone occupancy tracking
• System metrics (CPU, memory, disk)
• API status indicators

Hardware Configuration

• Interactive antenna array visualization
• Real-time CSI data display
• Configuration panels
• Hardware status monitoring

Data Sources

The sensing service (sensing.service.js) reports these connection states:

State	Banner Color	Description
LIVE - ESP32	Green	Connected to the Rust sensing server receiving real CSI data
RECONNECTING	Yellow (pulsing)	WebSocket disconnected, retrying (up to 20 attempts)
SIMULATED DATA	Red	Fallback to client-side simulation after 5+ failed reconnects
API TOKEN REQUIRED	Red	The server has RUVIEW_API_TOKEN set and this browser has no valid token. Retries stop; click the banner or open Settings → API Access

Simulated frames include a _simulated: true marker so code can detect synthetic data.

Backends

Rust Sensing Server (primary)

The Rust-based wifi-densepose-sensing-server serves the UI and provides:

• GET /health — server health
• GET /api/v1/sensing/latest — latest sensing features
• GET /api/v1/vital-signs — vital sign estimates (HR/RR)
• GET /api/v1/model/info — RVF model container info
• WS /ws/sensing — real-time sensing data stream
• WS /api/v1/stream/pose — real-time pose keypoint stream

Python FastAPI (legacy)

The original Python backend on port 8000 is still supported. The UI auto-detects which backend is available via backend-detector.js.

Quick Start

With Docker (recommended)

cd docker/

# Default: auto-detects ESP32 on UDP 5005, falls back to simulation
docker-compose up

# Force real ESP32 data
CSI_SOURCE=esp32 docker-compose up

# Force simulation (no hardware needed)
CSI_SOURCE=simulated docker-compose up

Open http://localhost:3000/ui/index.html

With local Rust binary

cd v2
cargo build -p wifi-densepose-sensing-server --no-default-features

# Run with simulated data
../../target/debug/sensing-server --source simulated --tick-ms 100 --ui-path ../../ui --http-port 3000

# Run with real ESP32
../../target/debug/sensing-server --source esp32 --tick-ms 100 --ui-path ../../ui --http-port 3000

Open http://localhost:3000/ui/index.html

With Python HTTP server (legacy)

# Start FastAPI backend on port 8000
wifi-densepose start

# Serve the UI on port 3000
cd ui/
python -m http.server 3000

Open http://localhost:3000

Pose Estimation Modes

Mode	Badge	Requirements	Accuracy
Signal-Derived	Green	1+ ESP32, no model needed	Presence, breathing, gross motion
Model Inference	Blue	4+ ESP32s + trained .rvf model	Full 17-keypoint COCO pose

To use model inference, start the server with a trained model:

sensing-server --source esp32 --model path/to/model.rvf --ui-path ./ui

Configuration

API Configuration

Edit config/api.config.js:

export const API_CONFIG = {
  BASE_URL: window.location.origin,
  API_VERSION: '/api/v1',
  WS_CONFIG: {
    RECONNECT_DELAY: 5000,
    MAX_RECONNECT_ATTEMPTS: 20,
    PING_INTERVAL: 30000
  }
};

Testing

Open tests/test-runner.html to run the test suite:

cd ui/
python -m http.server 3000
# Open http://localhost:3000/tests/test-runner.html

Test categories: API configuration, API service, WebSocket, pose service, health service, UI components, integration.

Styling

Uses a CSS design system with custom properties, dark/light mode, responsive layout, and component-based styling. Key variables in :root of style.css.

License

Part of the WiFi-DensePose system. See the main project LICENSE file.