| 基于MYO的肌电假肢手控制中手势在线识别系统 |
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| 引用本文： | 王朋,李传江,井本成,张崇明,张自强.基于MYO的肌电假肢手控制中手势在线识别系统[J].上海师范大学学报(自然科学版),2018,47(1):43-48. |
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| 作者姓名： | 王朋 李传江 井本成 张崇明 张自强 |
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| 作者单位： | 上海师范大学信息与机电工程学院; |
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| 摘 要： | 表面肌电(surface electromyography,s EMG)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%.
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| 关 键 词： | 表面肌电信号 MYO 特征提取 BP神经网络 在线识别 |
| 收稿时间： | 2016/6/12 0:00:00 |
Hand mode online recognition system of electromyography prosthetic hand based on MYO |
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| Wang Peng,Li Chuanjiang,Jing Bencheng,Zhang Chongming and Zhang Ziqiang.Hand mode online recognition system of electromyography prosthetic hand based on MYO[J].Journal of Shanghai Normal University(Natural Sciences),2018,47(1):43-48. |
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| Authors: | Wang Peng Li Chuanjiang Jing Bencheng Zhang Chongming and Zhang Ziqiang |
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| Institution: | The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China,The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China,The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China,The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China and 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%. |
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| Keywords: | surface electromyography MYO feature extraction BP neural network online recognition |
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