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.
作者
张龙娇
曾晓勤
ZHANG Longjiao;ZENG Xiaoqin(College of Computer and Information,Hohai University,Nanjing 211100,China)
出处
《计算机工程与应用》
CSCD
北大核心
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）
