文献类型：期刊文献

英文题名：Spatial and temporal attention embedded spatial temporal graph convolutional networks for skeleton based gait recognition with multiple IMUs

作者：Yan, Jianjun[1];Xiong, Weixiang[1];Jin, Li[2];Jiang, Jinlin[2];Yang, Zhihao[1];Hu, Shuai[1];Zhang, Qinghong[1]

机构：[1]East China Univ Sci & Technol, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China;[2]Shanghai Aerosp Control Technol Res Inst, Shanghai 201108, Peoples R China

年份：2024

卷号：27

期号：9

外文期刊名：ISCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001311287000001)】；

基金：ACKNOWLEDGMENTS This work was supported by the Major Research Plan of the National Natural Science Foundation of China (no. 91748110) .

语种：英文

外文关键词：The two-stream; capture different gait; patterns; user experiments

摘要：Gait recognition is one of the key technologies for exoskeleton robot control, while the current IMU-based gait recognition methods only use inertial data and do not fully consider the interconnections of human spatial structure and human joints. In this regard, a skeleton-based gait recognition approach with inertial measurement units using spatial temporal graph convolutional networks with spatial and temporal attention is proposed. A human forward kinematics solver module was used for constructing different human skeleton models and a temporal attention module was added for capturing the more important time frames in the gait cycle. Moreover, the two-stream structure was used to construct spatial temporal graph convolutional networks with spatial and temporal attention for gait recognition, and an average accuracy of about 99% was obtained in user experiments, which is the best performance compared to other algorithms, provides certain reference for gait recognition and real-time control of exoskeleton robots.