文献类型：期刊文献

英文题名：Auto-Correlation and Channel Attention Enhanced Deep Graph Convolution Networks for Gait Phase Prediction Based on Multi-IMU System

作者：Yan, Jianjun[1];Xu, Yingjia[1];Yang, Zhihao[1];Jin, Li[2];Jiang, Jinlin[2];Lin, Yue[1];Xiong, Weixiang[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, Servo Dept, Shanghai 201108, Peoples R China

年份：2024

卷号：17

期号：1

外文期刊名：INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS

收录：;EI(收录号：20243416898231);WOS:【SCI-EXPANDED(收录号:WOS:001289535600001)】；

基金：This research was funded by the Major Research Plan of the National Natural Science Foundation of China under grant number 91748110.

语种：英文

外文关键词：Gait phase prediction; Channel attention mechanism; Auto-Correlation mechanism; Deep graph convolutional networks; Skeleton model; Multiple inertial measurement units

摘要：Gait phase prediction is important in controlling assistive robotic devices such as exoskeletons, where the control unit must differentiate between gait phases to provide the necessary assistance when the user is wearing the exoskeleton. To achieve the objective of precisely identifying the gait phase of users for the accurate control of the exoskeleton, this study proposes Auto-Correlation and Channel Attention enhanced Deep Graph Convolutional Networks (ACCA-DGCN) for gait phase prediction, and a gait phase prediction model based on multiple inertial measurement units (IMUs) and skeleton graph was established, in order to fully utilize the dependency among joints, and enhance accuracy and reliability of gait phase prediction. First, a human lower limb gait data acquisition equipment was developed, and the gait data of human walking were collected. The skeleton graph of the human lower limb was constructed through the natural connection relationship of joints in the human skeleton. After that, the ACCA-DGCN-based gait phase prediction model was constructed by using the gait data of human walking. Auto-Correlation (AC) and Efficient Channel Attention (ECA) were introduced to effectively capture periodic features of gait data and focus on the channels with high contributions to gait phase prediction. Finally, the effect of the window size on the performance of the ACCA-DGCN model was explored, and the proposed algorithm was compared with the other five deep learning algorithms: CNN, RNN, TCN, LSTM, and DGCN. The experimental results show that the average accuracy of gait phase prediction model based on ACCA-DGCN reaches up to 92.26% and 97.21% in user-independent and user-dependent experiments, respectively, which is superior to the other five algorithms. This study provides a new method for gait phase prediction, which is useful for improving the control of exoskeleton robots.