Abstract

Info

Author(s):: ZHAO Pu1; WU Yi1; 2; 1. School of Electronics Information Engineering, Hebei University of Technology, Tianjin 300401, China 2. National Experiment Teaching Demonstration Center for Electronic and Communication Engineering, Hebei University of Technology, Tianjin 300401, China

Keywords:: Keywords: fall detection non-local attention mechanism loss function community healthcare

Abstract:: Abstract: A fall detection algorithm for community healthcare is proposed to avoid the secondary injury caused by untimely treatment when the elder living alone falls in the community. The algorithm has two branches, namely 2D convolution and 3D convolution, which allow it can extract spatial and temporal features simultaneously. The dense connections added in the 3D branch enhance the ability to extract temporal features the residual blocks in the 2D branch are redesigned to improve the ability of spatial feature extraction and a non-local attention mechanism is introduced to the branch fusion for better feature fusion. The algorithm also takes scene information into consideration, and it is supervised by SIoU loss function and the combined loss function to realize fall detection. The experiment on the expanded public URFD dataset reveals that the proposed method has a detection accuracy of 98.3%, which verifies its performance and robustness for fall detection.