中国组织工程研究

中国组织工程研究 ›› 2010, Vol. 14 ›› Issue (43): 8073-8076.doi: 10.3969/j.issn.1673-8225.2010.43.024

• 数字化骨科 digital orthopedics • 上一篇    下一篇

表面肌电信号的分析和特征提取

吴冬梅，孙  欣，张志成，杜志江   

  1. 哈尔滨工业大学机器人技术与系统国家重点实验室，黑龙江省哈尔滨市  150080
  • 出版日期:2010-10-22 发布日期:2010-10-22
  • 通讯作者: 杜志江，博士生导师，哈尔滨工业大学机器人技术与系统国家重点实验室，黑龙江省哈尔滨市 150080 duzj01@hit.edu.cn
  • 基金资助:

    国家“863”高科技资助项目(2009AA04Z 202)；新世纪优秀人才支持计划（NCET-07-0232）；哈尔滨工业大学科研创新基金(HIT.NSRIF.2009022)。

Feature collection and analysis of surface electromyography signals

Wu Dong-mei, Sun Xin, Zhang Zhi-cheng, Du Zhi-jiang   

  1. State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, Heilongjiang Province, China
  • Online:2010-10-22 Published:2010-10-22
  • Contact: Du Zhi-jiang, Doctoral supervisor, State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, Heilongjiang Province, China duzj01@hit.edu.cn
  • Supported by:

    the National High Technology Research and Development Program of China (863 Program), No. 2009AA04Z202; the Program for New Century Excellent Talents in University, No. NCET-07-0232; the Natural Scientific Research Innovation Foundation of Harbin Institute of Technology, No. HIT.NSRIF.2009022

PDF

2409

摘要：

背景：表面肌电信号的检测与分析对临床诊断人体功能状况以及患者康复具有重要意义。
目的：对表面肌电信号的采集、信号处理、特征分析和特征值提取方面进行分析。
方法：在人体屈伸肘部的过程中，选取人体上肢4块肌肉(肱三头肌，肘肌，肱二头肌，肱桡肌)分别检测表面肌电信号，对表面肌电信号进行陷波和带通滤波等预处理(优化)。在此基础上分析表面肌电信号的特征，并应用不同的特征值提取方法对优化后的表面肌电信号进行了特征提取。
结果与结论：时域方法最早应用于肌电信号分析，易提取、方法简单；频域方法提取的特征值较稳定，使得频域方法成为肌电信号处理技术的主流；以小波变换为代表的时-频分析方法因结合了时域、频域两方法的特性，在肌电信号分析方面颇有潜力。

关键词: 表面肌电信号, 信号采集, 信号处理(优化), 特征分析, 特征值提取

Abstract:

BACKGROUND: Analysis and feature extraction of surface electromyography signal (sEMG) has important meaning in the clinical diagnosis of human function and state and patients rehabilitation.
OBJECTIVE: To analyze sEMG collection, signal processing, extraction analysis and feature value extraction.
METHODS: sEMG was collected from 4 muscles in upper limb including triceps brachii, anconeus, biceps brachii and brachioradialis in the processing of human elbow flexion and stretch. Trapped wave and bandpass filtering were performed. sEMG features were analyzed, and the optimized sEMG features were extracted using different methods.
RESULTS AND CONCLUSION: Time domain method has been early used for sEMG analysis, which is easy and simple. Frequency domain-extracted features are stable and thereby it has become a main method. Wavelet transform time-frequency domain method combines features of two methods and exhibits potentials in analyzing sEMG.

中图分类号: