Multi-Sensor Fusion for Fall Detection and Early Warning: Overcoming Camera Blind Spots

The aging population has led to an increased need for reliable fall detection systems that can provide timely assistance to individuals who have fallen. This paper presents a multi-sensor fusion system designed to detect falls and issue early warnings, overcoming the limitations of camera-based systems, particularly in areas where camera surveillance is not feasible. We utilize a combination of an MPU6050 six-axis pose sensor and a MAX30102 heart rate and blood oxygen sensor to achieve high accuracy in fall detection. The system is designed to differentiate between falling and normal physical activities, reducing false positives. When a fall is detected, the system triggers an alert and sends real-time health data to a TCP server for monitoring on a mobile terminal. Our approach addresses camera blind spots and provides a comprehensive solution for fall detection in non-visual environments.