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RuView is an edge AI perception system that transforms ordinary WiFi signals into real-time environmental sensing and human pose estimation. Built on the concept of WiFi DensePose, it analyzes disturbances in WiFi Channel State Information (CSI) caused by human movement to reconstruct body position, breathing patterns, heart rate, and presence. Unlike traditional vision systems, RuView operates without cameras, wearables, or cloud connectivity, making it a privacy-first sensing solution. The system runs on low-cost hardware such as ESP32 sensor meshes and performs signal processing and machine learning directly at the edge. By learning the RF signature of each environment over time, RuView adapts automatically to different spaces and improves its sensing accuracy. Designed for applications ranging from healthcare monitoring to disaster response, it enables spaces to gain spatial awareness using the radio signals already present in the environment.
Features
- WiFi DensePose technology that reconstructs human body pose using only WiFi signal disturbances.
- Contactless vital sign monitoring that detects breathing and heart rate without wearables.
- Through-wall sensing that identifies presence, motion, and activity even without line-of-sight.
- Edge AI architecture running on low-cost ESP32 sensor meshes with no internet or cloud dependency.
- Self-learning models that adapt to each room’s RF signature and improve over time.
- Real-time sensing pipeline with REST APIs, WebSocket streaming, and visualization dashboards for monitoring environments.
Project Samples
