基于人工神经网络对sEMG信号的手势动作识别

王景芳; 施霖

doi:10.13873/j.1000-9787(2017)06-0063-03

Chinese

您当前的位置：

首页 >

文章列表页 >

基于人工神经网络对sEMG信号的手势动作识别

研究与探讨 | 更新时间：2025-09-11

- 基于人工神经网络对sEMG信号的手势动作识别
- Finger movements recognition based on artificial neural network on sEMG signal
- 传感器与微系统 2017年36卷第6期页码：63-65+69
- 作者机构：
  
  昆明理工大学信息工程与自动化学院
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目（61368005）
- DOI：10.13873/j.1000-9787(2017)06-0063-03
  中图分类号： TP183;TP391.41
- 纸质出版：2017
- 稿件说明：
移动端阅览
[1]王景芳,施霖.基于人工神经网络对sEMG信号的手势动作识别[J].传感器与微系统,2017,36(06):63-65+69.

WANG Jing-fang, SHI Lin. Finger movements recognition based on artificial neural network on sEMG signal[J]. 2017, 36(6): 63-65+69.
[1]王景芳,施霖.基于人工神经网络对sEMG信号的手势动作识别[J].传感器与微系统,2017,36(06):63-65+69. DOI： 10.13873/j.1000-9787(2017)06-0063-03.

WANG Jing-fang, SHI Lin. Finger movements recognition based on artificial neural network on sEMG signal[J]. 2017, 36(6): 63-65+69. DOI： 10.13873/j.1000-9787(2017)06-0063-03.

摘要

针对利用表面肌电信号（sEMG）对手势动作的肌电信号的研究较少和sEMG信号处理过于复杂的问题

提出了利用人工神经网络和sEMG信号对人的手势动作进行识别研究

引入了MYO硬件设备对新的手势动作sEMG信号采集。利用MYO从手臂上获取每一个手势动作的sEMG信号

提取信号特征值

作为算法的训练数据和测试数据。采用人工神经网络中的反向传递神经网络算法来进行对4种不同手势动作分类

对应目标手指识别率在90.35%。研究结果可以被用来做临床诊断和生物医学的应用以及用于现代硬件的发展和更现代化的人机交互的发展。

Abstract

Surface electromyography（ sEMG） signals for fingers action research

which finger movements of the research methods is very less and signal processing is too complex. This experiment using the artificial neural network and sEMG signal to the person’s finger gestures recognition research. The experiment introduced the MYO hardware equipment of the new finger movements sEMG signal acquisition. Use MYO from arm for each finger movements of sEMG signal

and then to extract the signal characteristic value as training data and test data of the algorithm

the recognition algorithm with artificial neural network of back propagation（ BP） algorithm for the classification of four different finger gestures

its corresponding target finger recognition rate at 90. 35 %. The result can be used for clinical diagnosis and biomedical applications

the development of modern hardware and more modern the development of human-computer interaction.

关键词

Keywords

references

多传感器数据融合技术研究进展 [J]. 黄漫国,樊尚春,郑德智,邢维巍. 传感器与微系统 . 2010(03)

Time series modeling of surface EMG based hand manipulation identification via expectation maximization algorithm [J] . Yang Lu,Zhaojie Ju,Yurong Liu,Yuxuan Shen,Honghai Liu. Neurocomputing . 2015

Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees [J] . F. Riillo,L.R. Quitadamo,F. Cavrini,E. Gruppioni,C.A. Pinto,N. Cosimo Pastò,L. Sbernini,L. Albero,G. Saggio. Biomedical Signal Processing and Control . 2014

Characterizing EMG data using machine-learning tools [J] . Jamileh Yousefi,Andrew Hamilton-Wright. Computers in Biology and Medicine . 2014

Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders [J] . Abdulhamit Subasi. Computers in Biology and Medicine . 2013

An integrated intelligent computing model for the interpretation of EMG based neuromuscular diseases [J] . Babita Pandey,R.B. Mishra. Expert Systems With Applications . 2009 (5)

浏览量

311

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于FMCW雷达的非接触式情感识别

基于高效通道注意力网络的人脸表情识别

基于改进YOLOv3的有遮挡行人检测

基于电子鼻的呼气无创肝癌检测方法研究

基于改进深度森林的小目标检测算法