@inproceedings{df0f2e88dfbe4bcdbb018c9a1b680524,
title = "GTGR-Net: Graph Attentional-Temporal Network for Surface-Electromyography-Based Gesture Recognition",
abstract = "In this process of active rehabilitation assisted by hand rehabilitation robot, the patient's hand motion intention, that is, the patient's gesture recognition, plays an important role. Gesture recognition based on sEMG signal is a hot research topic. Due to the spatial correlation and time non-stationary of sEMG signal, this research topic has many difficulties. In order to solve this problem, we come up with a gesture recognition network GTGR-Net based on sEMG signal, which uses the combination of graph attention network and time convolution network to extract the spatiotemporal information of sEMG signal. We verify the effect of our algorithm on three public data sets and achieve good results, which is better than the other ways.",
keywords = "gesture recognition, Graph attention network, graph structure, sEMG, temporal convolutional network",
author = "Xiaoxu Jia and Hongbo Wang and Jingjing Luo and Zhiping Lai and Xueze Zhang and Weiqi Zhang and Xiuhong Tang",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 3rd International Conference on Computing, Networks and Internet of Things, CNIOT 2022 ; Conference date: 20-05-2022 Through 22-05-2022",
year = "2022",
doi = "10.1109/CNIOT55862.2022.00039",
language = "English (US)",
series = "Proceedings - 2022 3rd International Conference on Computing, Networks and Internet of Things, CNIOT 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "182--185",
booktitle = "Proceedings - 2022 3rd International Conference on Computing, Networks and Internet of Things, CNIOT 2022",
address = "United States",
}