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.
Original language | English (US) |
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Title of host publication | 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 |
Number of pages | 4 |
ISBN (Electronic) | 9781665469104 |
DOIs | |
State | Published - 2022 |
Event | 3rd International Conference on Computing, Networks and Internet of Things, CNIOT 2022 - Qingdao, China Duration: May 20 2022 → May 22 2022 |
Publication series
Name | Proceedings - 2022 3rd International Conference on Computing, Networks and Internet of Things, CNIOT 2022 |
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Conference
Conference | 3rd International Conference on Computing, Networks and Internet of Things, CNIOT 2022 |
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Country/Territory | China |
City | Qingdao |
Period | 05/20/22 → 05/22/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- gesture recognition
- Graph attention network
- graph structure
- sEMG
- temporal convolutional network
ASJC Scopus subject areas
- Information Systems and Management
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
- Information Systems