Chinese Journal of Tissue Engineering Research ›› 2010, Vol. 14 ›› Issue (43): 8073-8076.doi: 10.3969/j.issn.1673-8225.2010.43.024

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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

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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.

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