ウ　ジンソク   WOO JINSEOK   禹　珍碩    所属   工学部 機械工学科    職種   専任講師
言語種別	英語
発行・発表の年月	2024/11
形態種別	国際会議論文
査読	査読あり
標題	Enhanced Gesture Recognition via Interpretable Temporal Convolutional Networks Utilizing Spatio-Temporal sEMG Features
執筆形態	共著
掲載誌名	IEEE CyberSciTech/DASC/PICom/CBDCom 2024
掲載区分	国外
出版社・発行元	IEEE
巻・号・頁	pp.749-754
総ページ数	6
国際共著	国際共著
著者・共著者	Xiaohu Ao, Jinhua She, Feng Wang, Seiichi Kawata, Edwardo Fumihiko Fukushima, Jinseok Woo
概要	Rehabilitation robots and human-robot interaction systems are receiving increasing attention in modern healthcare and engineering. Motion intent recognition techniques based on surface electromyography (sEMG) signals provide important support for these systems. However, motion intent recognition models rarely focus on the spatiotemporal properties of sEMG signals simultaneously. There is a lack of interpretable analysis of these spatiotemporal properties. The purpose of this study is to perform gesture recognition of sEMG signals from the Ninapro database using a temporal convolutional network (TCN). The sEMG signals are converted into sEMG images, which are used as input to the TCN model. The TCN model is utilized to perform gesture recognition on these images. The results show that after fusing the spatial and temporal features of the sEMG signal, the recognition accuracy of the TCN model is significantly higher than that of models using only spatial features or only temporal features. To understand the decision-making process of the model, we used an interpretable method based on Shapley values to perform a detailed interpretive analysis of the recognition results. The experimental results show that the TCN model has significant accuracy and stability in the sEMG signal gesture recognition task, while the Shapley value-based interpretable method can effectively reveal the contribution of different features to the prediction results, thus improving the transparency and user trust in the model. This study not only provides a new technical tool for sEMG signal gesture recognition but also demonstrates the value of interpretable methods in deep learning models.
外部リンクURL	https://cyber-science.org/2024/