A Novel Fall Detection Scheme Using Yolo-Pose with Efficient Attention and Lightweight Convolution

Falling is a prominent external cause of severe injuries and death among the elderly. To this end, fall detection serves to mitigate these hazards. This paper outlines the need for fall detection systems and surveys the proposed methodologies, citing several disadvantages and advantages. In these respects, we propose a fall detection system based on a threshold classification approach and the improved YOLO-pose model, the latter involving attention mechanism-related enhancements and increased convolutions to enhance the accuracy and speed of pose estimation. This paper wants to evaluate this proposed addition to the original one. The paper tests the proposed system under multiple scenarios to demonstrate efficacy in practical applications within elder care environments. Finally, we evaluate the performance of our model and go through plans regarding this fall-detection system.