基于深度神经网络的sEMG手势识别研究  

Research on Gesture Recognition of sEMG Based on Deep Neural Network

作　　者：张龙娇 曾晓勤[1] ZHANG Longjiao;ZENG Xiaoqin(College of Computer and Information,Hohai University,Nanjing 211100,China)

机构地区：[1]河海大学计算机与信息学院

出　　处：《计算机工程与应用》2019年第23期113-119,共7页Computer Engineering and Applications

基　　金：国家重点研发计划项目（No.2017YFC0405805）

摘　　要：为了提高表面肌电信号(sEMG)手势识别算法的准确性,并解决人为提取大量特征具有局限性的问题,提出了一种基于深度神经网络的手势识别方法。将MYO臂环采集到的8通道sEMG数据,采用活动段分割的方法探测到有效动作;设计出一种融合卷积神经网络(CNN)和长短时记忆(LSTM)网络的神经网络;实验的结果表明手势识别准确率为91.6%,验证了提出的方案高效可行。In order to improve the accuracy of sEMG gesture recognition algorithm and solve the limitation caused by extracting a large number of features artificially, this paper proposes a gesture recognition method based on deep neural network. Firstly, it uses an active segment segmentation method on 8 channel sEMG data which is collected by MYO armband to detect effective actions. Then, it designs a neural network which combines Convolutional Neural Network(CNN)and Long-Short Term Memory network(LSTM). The result shows that the accuracy of gesture recognition reaches91.6% and the proposed method is proved to be efficient and feasible.

关 键 词：表面肌电信号 手势识别 MYO臂环 卷积神经网络 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]