| 摘要: |
| 表面肌电（surface electromyography，sEMG）信号被广泛应用于临床诊断、康复工程和人机交互等领域中.针对目前控制肌电假肢手的电极成本高、电极佩戴困难以及操作灵活性差等问题，设计一种基于MYO的肌电假肢手手势在线识别系统.通过采集人体上肢前臂的表面肌电信号，在时域上分别提取5种特征值，利用反向传播（back propagation，BP）神经网络分类算法实现对8种手势动作意图的在线实时识别.实验结果证明，利用MYO进行手势识别可以获得较好的识别结果，该系统能够准确识别8种手部动作，平均在线识别率达到92%. |
| 关键词: 表面肌电信号 MYO 特征提取 BP神经网络 在线识别 |
| DOI：10.3969/J.ISSN.1000-5137.2018.01.007 |
| 分类号: |
| 基金项目: |
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| Hand mode online recognition system of electromyography prosthetic hand based on MYO |
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Wang Peng, Li Chuanjiang, Jing Bencheng, Zhang Chongming, Zhang Ziqiang
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The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China
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| Abstract: |
| Surface electromyography (sEMG) is widely used in clinical diagnosis,rehabilitation engineering and human-computer interaction,etc.Aming at the problems of high cost of electrodes to control electromyography prosthetic hands,the difficulty in electrodes wear and poor operation flexibility,a MYO-based hand mode online identification system of electromyography prosthetic hands is designed.By collecting the sEMG of the human upper-limb-forearm and extracting 5 characteristic values in the time domain,8 real-time gesture recognition strategies are realized through the back propagation neural network.Experimental results show that the MYO-based gesture recognition canproduce bettergesture recognition results.The system can accurately identify the eight kinds of hand movements,and the average online recognition rate reaches 92%. |
| Key words: surface electromyography MYO feature extraction BP neural network online recognition |
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