DocumentCode
3045078
Title
Novel time-frequency approach for muscle fatigue detection based on sEMG
Author
Fengjun Bai ; Lubecki, Tomasz Marek ; Chee-Meng Chew ; Chee-Leong Teo
Author_Institution
Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore, Singapore
fYear
2012
fDate
28-30 Nov. 2012
Firstpage
364
Lastpage
367
Abstract
Muscle fatigue is the decrease in its ability to generate a target force. In this paper, a novel muscle fatigue detection algorithm based on sEMG signal is developed. Short-time Fourier transform (STFT) and continuous wavelet transform (CWT) are used to extract features of sEMG signal (mean frequency and signal power). Further signal power and the estimated power relative changes are calculated to derive the fatigue evaluation and generate the general fatigue levels. sEMG signals generated from subjects´ Biceps Brachii muscles under different muscle contraction trials were used to evaluate the feasibility and effectiveness of the proposed fatigue detection methods. Results from STFT and CWT are compared. The results also show that the proposed method is reliable in the analysis of muscle fatigue during isometric and dynamic contractions and quantifying the discrete muscle fatigue levels.
Keywords
Fourier transforms; biomechanics; cellular biophysics; electromyography; feature extraction; medical signal processing; wavelet transforms; EMG signal; bicep brachii muscles; continuous wavelet transform; discrete muscle fatigue levels; dynamic contractions; estimated power relative changes; fatigue detection methods; fatigue evaluation; feature extraction; isometric contractions; muscle contraction trials; muscle fatigue detection algorithm; short-time Fourier transform; signal power; target force; time-frequency approach; Continuous wavelet transforms; Dynamics; Fatigue; Force; Muscles; Time frequency analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Biomedical Circuits and Systems Conference (BioCAS), 2012 IEEE
Conference_Location
Hsinchu
Print_ISBN
978-1-4673-2291-1
Electronic_ISBN
978-1-4673-2292-8
Type
conf
DOI
10.1109/BioCAS.2012.6418421
Filename
6418421
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