ruvnet

RuView

AI#ESP32#WiFi#Machine Learning#Edge Computing#Signal Processing

// summary

RuView is an edge-based sensing platform that utilizes WiFi Channel State Information (CSI) to detect human presence, vital signs, and activities without the need for cameras or wearables. The system processes radio signal disturbances through low-cost ESP32 hardware to provide real-time spatial intelligence and environment mapping. It supports advanced features like 3D point cloud generation, pose estimation, and persistent data storage using local neural networks.

// technical analysis

RuView is a specialized WiFi sensing platform that leverages Channel State Information (CSI) from low-cost ESP32 hardware to transform ambient radio signals into spatial intelligence. By utilizing spiking neural networks and multi-frequency mesh scanning, it enables contactless monitoring of human presence, vital signs, and activity recognition without the need for cameras or cloud connectivity. The project prioritizes edge-native processing and cryptographic attestation, offering a privacy-focused alternative to traditional surveillance systems while addressing the inherent challenges of signal noise and spatial resolution through advanced signal processing and multi-node fusion.

// key highlights

Enables contactless monitoring of breathing and heart rate by analyzing subtle disturbances in WiFi radio waves.

Provides real-time occupancy and presence detection that functions through walls and in complete darkness.

Supports 17-keypoint pose estimation using a camera-free training pipeline that achieves 92.9% accuracy.

Utilizes a multi-frequency mesh architecture that turns neighboring routers into passive radar illuminators for enhanced sensing.

Features a persistent edge-AI system that stores sensing events as searchable vectors for anomaly detection and room fingerprinting.

Implements spiking neural networks that allow the system to adapt to new environments in under 30 seconds.

// use cases

Contactless vital sign monitoring including heart rate and breathing rate detection

Real-time human presence detection, occupancy counting, and activity recognition

Through-wall environment mapping and 3D point cloud generation using WiFi signals

// getting started

To begin, you can either run the Docker image for simulated data evaluation or flash the provided firmware onto ESP32-S3 hardware for live sensing. Once the hardware is provisioned with your WiFi credentials, you can execute the provided Node.js scripts to perform tasks like RF scanning, person counting, or real-time pose estimation. For advanced features, integrate a Cognitum Seed to enable persistent storage, cryptographic attestation, and AI-driven agent reasoning.