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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\u00a0a gait phase prediction model\u00a0based on multiple inertial measurement units (IMUs) and skeleton graph was established, in order to fully utilize the dependency among\u00a0joints, and enhance accuracy and reliability of\u00a0gait 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\u00a0in the human skeleton. After that, the ACCA-DGCN-based gait phase prediction model was constructed by using the gait data\u00a0of\u00a0human walking. Auto-Correlation (AC) and Efficient Channel Attention (ECA) were introduced to\u00a0effectively capture periodic features of gait data and focus\u00a0on 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\u00a0compared with the other\u00a0five deep learning\u00a0algorithms: 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\u00a097.21%\u00a0in user-independent\u00a0and 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.<\/jats:p>","DOI":"10.1007\/s44196-024-00603-8","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T07:02:45Z","timestamp":1723446165000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Auto-Correlation and Channel Attention Enhanced Deep Graph Convolution Networks for Gait Phase Prediction Based on Multi-IMU System"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1233-1136","authenticated-orcid":false,"given":"Jianjun","family":"Yan","sequence":"first","affiliation":[]},{"given":"Yingjia","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Zhihao","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Li","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Jinlin","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Weixiang","family":"Xiong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"603_CR1","doi-asserted-by":"publisher","unstructured":"Walsh, C.J., Paluska, D., Pasch, K., Grand, W., Valiente, A., Herr, H.: Development of a lightweight, underactuated exoskeleton for load-carrying augmentation. 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