Enhanced Fall Detection for Seniors with Sensor Fusion and Optimized AI Techniques

Abstract

Senior citizens often prefer privacy and may live alone in secluded homes, making fall-related injuries a serious concern, especially during nighttime or in washrooms, where immediate assistance is unavailable. This study proposes an AI-based fall detection approach utilizing particle swarm optimization (PSO) to enhance the accuracy of a sensor fusion mechanism integrated with biomarkers for improved fall assessment. The PSO algorithm optimizes feature selection, refining sensor data interpretation to reduce false alarms while ensuring reliable detection. The analysis is conducted using MATLAB, demonstrating promising insights into the effectiveness of the proposed method in real-world elder care applications.