Hand Gesture Recognition Using Instant High-density EMG Graph via Deep Learning Method
文献类型：会议论文
| 作者 | Xiong DZ(熊德臻)1,2,3; Zhang DH(张道辉)2,3 ; Zhao XG(赵新刚)2,3; Zhao YW(赵忆文)2,3
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| 出版日期 | 2020 |
| 会议日期 | November 6-8, 2021 |
| 会议地点 | Shanghai, China |
| 关键词 | Electromyography (EMG) instant EMG graph deep learning RNN |
| 页码 | 5143-5148 |
| 英文摘要 | Electromyography (EMG) shows excellent potential for human-machine interaction (HMI) tasks. It reflects the physiological intention of human beings, which contributes to a more intuitive human-machine interface. The sequence of EMG signals acquiring during a period is most commonly used for feature extraction and gesture recognition. The instant EMG graph, which is always thought to be useless due to too much noise inside it, can also be used to recognize movement intention. This work explores a new path to recognize EMG patterns without feature engineering, using both deep learning and machine learning methods. This paper proposes a novel scheme to classify hand gestures through the instant graph of high-density electromyography (HD-EMG) using the deep learning method. Four types of recurrent neural networks (RNNs) with units including long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional version of them are chosen to extract temporal information automatically from EMG data. By a simple 100 frame majority voting, which corresponds to a 100ms window, the best performance of 98.57% is achieved by Bi-LSTM. Besides, the machine learning-based method also achieves an accuracy of 95.66%, which shows the instant EMG graph method's great potential. |
| 产权排序 | 1 |
| 会议录 | Proceedings - 2020 Chinese Automation Congress, CAC 2020
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-7281-7687-1 |
| WOS记录号 | WOS:000678697005047 |
| 源URL | [http://ir.sia.cn/handle/173321/28367]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Zhao XG(赵新刚) |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China |
| 推荐引用方式 GB/T 7714 | Xiong DZ,Zhang DH,Zhao XG,et al. Hand Gesture Recognition Using Instant High-density EMG Graph via Deep Learning Method[C]. 见:. Shanghai, China. November 6-8, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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