Muscle Fatigue Analysis for Healthy Adults Using TVAR Model with Instantaneous Frequency Estimation

The objective of this paper is to design a nonstationary time-varying autoregressive (TVAR) cascaded model to analyze electromyography (EMG) signals by using instantaneous frequency for muscle fatigue assessment. EMG is commonly used in the muscle fatigue study during muscle contractions by analyzin...

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Bibliographic Details
Published in2006 Proceeding of the Thirty-Eighth Southeastern Symposium on System Theory pp. 244 - 247
Main Authors Al zaman, A., Ferdjallah, M., Khamayseh, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Electromyography
Fatigue
Fourier transforms
Frequency estimation
Muscles
Signal analysis
Signal design
Signal representations
Signal resolution
Time frequency analysis
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Summary:The objective of this paper is to design a nonstationary time-varying autoregressive (TVAR) cascaded model to analyze electromyography (EMG) signals by using instantaneous frequency for muscle fatigue assessment. EMG is commonly used in the muscle fatigue study during muscle contractions by analyzing myoelectric signal spectrum. To validate the findings, our results are compared with the conventional short time Fourier transform (STFT) method. STFT has limitations in joint time frequency resolution for long intervals, whereas TVAR models overcome these limitations for nonstationary signals. In this study, EMG data recorded from the rectus femoris muscle are used to characterize muscular fatigue. Characterizations are done by using mean frequencies (MNF). According to our results, the new method has a better accuracy in signal representation, frequency resolution and joint time distribution
ISBN:9780780394575
0780394577
ISSN:0094-2898
2161-8135
DOI:10.1109/SSST.2006.1619081