Study on fatigue feature from forearm SEMG signal based on wavelet analysis

Baikun Wan, Lifeng Xu, Yue Ren, Lu Wang, Shuang Qiu, Xiaojia Liu, Xiuyun Liu, Hongzhi Qi, Dong Ming, Weijie Wang

    Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

    21 Citations (Scopus)


    The aim of this paper is to estimate muscle fatigue by using wavelet analysis method in SEMG signal analysis. A signal acquisition system is designed and forearm muscle fatigue experiments under static and dynamic contractions are performed. The wavelet analysis method is proposed to group the wavelet coefficients of SEMG signal into high frequency-band (100Hz-350Hz) and low frequency-band (13-22Hz). The amplitude of SEMG signal is determined by calculating the root mean square, the amplitude of high frequency is correlated to the force level and the amplitude of low frequency band which is correlated to the muscle fatigue shows an upward trend. Then correlation coefficients between RMS of low frequency band and MF, RMS of low frequency band and MDF in static contraction as well the first time-varying parameter in dynamic contraction are calculated. Results demonstrate that the wavelet analysis method is an effective analysis tool in muscle fatigue evaluation and it lays a foundation for studying at the muscle fatigue in a variety of muscle contraction modes.
    Original languageEnglish
    Title of host publication2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010
    Place of PublicationPiscataway
    PublisherIEEE Computer Society
    Number of pages4
    ISBN (Electronic)9781424493173
    ISBN (Print)9781424493180, 9781424493197
    Publication statusPublished - 2010


    • Muscle fatigue
    • Wavelet analysis
    • RMS
    • Correlation


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