Brief Description
Developed an AI-powered health monitoring system that uses contactless radar, imaging, and audio sensors to detect early signs of illness in sleeping individuals through biometric analysis.
Innovation Overview
Co-invented a breakthrough contactless health monitoring technology that combines radar sensing, computer vision, and machine learning to detect illness onset during sleep. The system establishes baseline biometric profiles (respiration rate, heart rate) over multiple days, then uses statistical analysis to identify deviations that may indicate fever or other medical conditions.
Key Technical Innovations
Contactless Detection: Uses low-power radar, infrared imaging, and audio processing to monitor breathing and heart rate without physical contact
AI-Driven Analysis: Employs machine learning algorithms to distinguish between normal sleep patterns (including REM cycles) and illness-related biometric changes
Early Warning System: Provides real-time alerts to caregivers when statistical deviations exceed predetermined thresholds
Multi-Modal Sensing: Integrates radar, visual, and audio data for robust biometric extraction and sleep state determination
Technical Achievement
Translated complex embedded systems expertise into a consumer health IoT product with cloud connectivity and mobile app integration. The system balances sensitivity with false alarm reduction through sophisticated algorithmic approaches.
Applications
Particularly valuable for infant monitoring, elderly care, and remote patient monitoring where early illness detection is critical. The contactless nature makes it ideal for continuous monitoring without disrupting sleep patterns.