Design and Evaluation of a Decision-Level Multisensor Fusion System for Indoor Fall Detection

Falls represent a critical public health concern, particularly for older adults and individuals with disabilities, often resulting in serious injuries, loss of independence, and increased mortality. Traditional fall detection systems suffer from high false positive rates, privacy concerns, and limited real-world applicability. This study presents an improved non-wearable fall detection method integrating YOLOv8-based skeletal pose estimation with load distribution sensing through decision-level sensor fusion. The system employs YOLOv8 for real-time human posture analysis and skeletal motion-based fall recognition, addressing limitations of simple presence-based or LiDAR-only approaches. A distributed Force-Sensitive Resistor (FSR) array embedded in the sensing surface monitors relative floor-loading patterns and impact-related changes. Decision-level fusion with temporal filtering combines skeletal postural and motion information with floor-load evidence, enabling accurate fall detection with substantially fewer false positives than the vision-only configuration. Key methodologies include detailed load sensor calibration, time-domain filtering to distinguish intentional lying from falls, and fusion logic leveraging the complementary strengths of visual and floor-based FSR sensing. Experimental evaluation in a controlled indoor environment configured to represent a residential setting achieved an F1-score of 0.935, specificity of 97.5%, and an 82% reduction in false-positive count compared with the vision-only configuration. This modular ROS2-based system provides a practical, non-intrusive framework with potential for future deployment in residential and assisted-living settings, while supporting extension to broader assistive monitoring applications.