Fall detection algorithm for community healthcare
10.3969/j.issn.1005-202X.2023.12.006
- VernacularTitle:面向社区医疗的跌倒检测算法
- Author:
Pu ZHAO
1
;
Yi WU
Author Information
1. 河北工业大学电子信息工程学院,天津 300401
- Keywords:
fall detection;
non-local attention mechanism;
loss function;
community healthcare
- From:
Chinese Journal of Medical Physics
2023;40(12):1486-1493
- CountryChina
- Language:Chinese
-
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.
