计算机工程与应用 ›› 2019, Vol. 55 ›› Issue (23): 113-119.DOI: 10.3778/j.issn.1002-8331.1902-0210

• 模式识别与人工智能 • 上一篇    下一篇

基于深度神经网络的sEMG手势识别研究

张龙娇,曾晓勤   

  1. 河海大学 计算机与信息学院,南京 211100
  • 出版日期:2019-12-01 发布日期:2019-12-11

Research on Gesture Recognition of sEMG Based on Deep Neural Network

ZHANG Longjiao, ZENG Xiaoqin   

  1. College of Computer and Information, Hohai University, Nanjing 211100, China
  • Online:2019-12-01 Published:2019-12-11

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。

关键词: 表面肌电信号, 手势识别, MYO臂环, 卷积神经网络

Abstract: 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 reaches 91.6% and the proposed method is proved to be efficient and feasible.

Key words: sEMG, gesture recognition, MYO armband, convolution neural network