Literature DB >> 21097104

Fourier and wavelet spectral analysis of EMG signals in isometric and dynamic maximal effort exercise.

José L Dantas1, Thiago V Camata, Maria A C Brunetto, Antonio C Moraes, Taufik Abrão, Leandro R Altimari.   

Abstract

Frequency domain analyses of changes in electromyographic (EMG) signals over time are frequently used to assess muscle fatigue. Fourier based approaches are typically used in these analyses, yet Fourier analysis assumes signal stationarity, which is unlikely during dynamic contractions. Wavelet based methods of signal analysis do not assume stationarity and may be more appropriate for joint time-frequency domain analysis. The purpose of this study was to compare Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) in assessing muscle fatigue in isometric and dynamic exercise. The results of this study indicate that CWT and STFT analyses give similar fatigue estimates (slope of median frequency) in isometric and dynamic exercise (P>0.05). However, the results of the variance was lower for both types of exercise in CWT compared to STFT (P < 0.05) indicating more variability in the EMG signal analysis using STFT. Thus, the stationarity assumption may not be the sole factor responsible for affecting the Fourier based estimates.

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Year:  2010        PMID: 21097104     DOI: 10.1109/IEMBS.2010.5627579

Source DB:  PubMed          Journal:  Annu Int Conf IEEE Eng Med Biol Soc        ISSN: 2375-7477


  4 in total

1.  A Real-Time EMG-Based Fixed-Bandwidth Frequency-Domain Embedded System for Robotic Hand.

Authors:  Biao Chen; Chaoyang Chen; Jie Hu; Thomas Nguyen; Jin Qi; Banghua Yang; Dawei Chen; Yousef Alshahrani; Yang Zhou; Andrew Tsai; Todd Frush; Henry Goitz
Journal:  Front Neurorobot       Date:  2022-06-30       Impact factor: 3.493

Review 2.  EMG Processing Based Measures of Fatigue Assessment during Manual Lifting.

Authors:  E F Shair; S A Ahmad; M H Marhaban; S B Mohd Tamrin; A R Abdullah
Journal:  Biomed Res Int       Date:  2017-02-19       Impact factor: 3.411

3.  Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor.

Authors:  G Ramos; J R Vaz; G V Mendonça; P Pezarat-Correia; J Rodrigues; M Alfaras; H Gamboa
Journal:  J Healthc Eng       Date:  2020-01-07       Impact factor: 2.682

4.  Performance during a 20-km cycling time-trial after caffeine ingestion.

Authors:  Henrique Bortolotti; Leandro Ricardo Altimari; Marcelo Vitor-Costa; Edilson Serpeloni Cyrino
Journal:  J Int Soc Sports Nutr       Date:  2014-08-30       Impact factor: 5.150

  4 in total

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