Vision and Sensor Fusion for Non-Invasive Health Monitoring

With increasing elderly populations worldwide, there has been a need for effective, contactless health monitoring systems. This research presents a smart, contactless system that combines computer vision, depth, multispectral, and ultraviolet (UV) sensors to track health parameters important in eldercare. The system detects the presence of humans automatically based on visual responses and turns on sensors only as necessary, minimizing power consumption. Critical physiological and environmental parameters like skin hydration, posture stability, fall incidents, and UV overexposure risks are obtained from AS7341, VL53L0X, TFmini Plus, and GUVAS12SD sensor data. Validations in real-world conditions yielded correct fall detection (>90% sensitivity), trustworthy hydration estimation based on near-infrared analysis, and prompt recognition of UV overexposure risk. Correlations between sensor types were established based on data confirmations, affirming system dependability. Conceived with scalability and real-time capabilities, this inexpensive setup is open to future integration with machine learning algorithms and mobile notification, making it an exciting device for ambient, non-invasive monitoring of health in the aging population.