ruvnet/RuView
RuView
RuView is a WiFi spatial sensing platform that uses radio frequency reflections to detect presence, track body poses, and monitor vital signs without the use of cameras. It functions as a 3D point-cloud spatial mapper, converting signal disturbances into coordinate sets to visualize physical environments and human movement.
The system operates as a distributed sensing mesh where synchronized nodes use consensus and shared audit trails to maintain data consistency across a swarm. It further acts as an MQTT home automation bridge, streaming real-time spatial telemetry and occupancy data to smart home ecosystems.
The platform covers health and wellness monitoring through contactless measurement of heart rate, respiration, and sleep quality, as well as the detection of falls and physical activity. Its spatial intelligence capabilities include crowd occupancy analysis, body pose estimation, and environmental mapping using radio frequency fingerprinting.
Integration is supported via standardized tool protocols that expose sensing data to AI agents and the deployment of signed binary modules to add specific hardware-level capabilities.
Features
- Point Cloud Spatial Mapping - Converts radio signal disturbances into 3D point-clouds to visualize physical environments and human movement.
- Contactless Vital Sign Monitors - Tracks heart rate and respiration patterns using WiFi signals without requiring physical sensors.
- Pose Estimation - Estimates human body poses and movement patterns using radio signal reflections.
- Fall Detection - Recognizes physical actions such as walking or falling using wireless signal monitoring.
- Presence and Location - Identifies the number of people in a room and tracks entries and exits via signal disturbances.
- Sleep Tracking - Monitors overnight sleep stages and screens for apnea using contactless radio sensing.
- Physiological Signal Estimators - Measures heart rate and respiration rates through radio signal variance without physical contact.
- 3D Point-Cloud Spatial Mappers - Converts radio signal disturbances into 3D coordinate sets for mapping physical environments and movement.
- Motion Retargeting - Maps real-time pose estimation and sensor feeds to 3D skeletal animations for activity monitoring.
- Spatial Point Cloud Renderers - Visualizes signal analysis as a 3D point cloud to map physical presence and movement.
- Non-Visual Presence Detectors - Identifies people in a room and tracks their movements using WiFi signals instead of cameras.
- Signal Visualizers - Renders signal feeds as a point-cloud viewer for spatial intelligence analysis.
- Temporal Pattern Analysis - Identifies human activities like falls or sleep apnea by analyzing variations in radio wave frequency over time.
- WiFi Spatial Mappers - Converts signal reflections into 3D point clouds to visualize physical environments and body poses.
- WiFi Spatial Sensing - Translates WiFi signal disturbances into 3D coordinate sets to detect presence and posture without cameras.
- WiFi Spatial Sensing Platforms - Uses WiFi signal reflections to detect presence, track body pose, and monitor vital signs without cameras.
- Agent Tooling Protocols - Exposes sensor data to AI agents via standardized tool protocols for automated querying and control.
- AI Agent Tooling - Exposes real-time occupancy and health data to AI agents via standardized tool protocols.
- Agent-to-Server Bridges - Provides interfaces that bridge real-time presence and vital sign data to AI agents.
- Swarm Cluster Management - Coordinates a mesh of sensing nodes to maintain data consistency across a distributed swarm.
- Smart Home Bridges - Integrates spatial sensing data into home automation ecosystems via MQTT and Matter bridges.
- Crowd Occupancy Analyzers - Provides the ability to count people and track multiple individuals simultaneously using signal density.
- RF Fingerprinting - Identifies specific rooms and detects moved or new objects using radio frequency fingerprinting.
- Distributed Consensus Protocols - Implements distributed consensus protocols to synchronize data across a mesh of sensing nodes.
- Real-time Event Streams - Streams real-time sensing telemetry to external platforms using an event-driven MQTT publish-subscribe model.
- Distributed Sensing Meshes - Synchronizes a network of sensing nodes using distributed consensus and shared audit trails.
- Outils de développement - Spatial sensing tool using WiFi signals.
- General Utilities - Privacy-preserving human pose estimation system.
- Notes & Productivity - Privacy-preserving human pose estimation.
