Literature DB >> 21708471

Wavelet-based intensity analysis of mechanomyographic signals during single-legged stance following fatigue.

W Jeffrey Armstrong1.   

Abstract

The von Tscharner (2000) "intensity analysis" describes the power of a non-stationary signal as a function of both frequency and time. The present study applied a version of this intensity analysis that utilizes Morlet wavelets as a means of gaining insight into the application of this technique as alternative to power spectral analysis for the evaluation of postural control strategy during the single-legged stance and to examine the effects of fatigue. Ten subjects (gender balanced, age: 25±3 years; height: 169.4±11.7 cm; weight: 79.0±16.9 kg) participated in two trials consisting of five 15-s dominant-leg stances. Three-uniaxial accelerometers were fixed to the surface of the dominant leg corresponding to VM, VL, SOL, and MMG was recorded at a sampling rate of 1000 Hz. Signals were later analyzed using a variation of the von Tscharner intensity analysis consisting of a filter bank of 11 Morlet wavelets (range: 2.1-131.1Hz). Two Wingate anaerobic tests (WAnT) separated by a 2-min rest were performed to introduce fatigue. Repeated measures ANOVAs showed significant effects for time, gender, trial, and wavelet (p<0.001) and significant interactions for muscle by wavelet, gender by trial, trial by wavelet, and gender by trial by wavelet (p<0.001). Peak total MMG intensity (mean±SD) was higher in males than females and higher following fatiguing exercise preWAnT (squared ms(-2)): 42.6±4.5 vs. 19.2±2.3; postWAnT (squared ms(-2)): 90.4±9.1 vs. 28.4±2.8. Peak total MMG intensity was compressed to the lower frequencies surrounding ∼12 Hz, corresponding to what might be considered physiologic tremor, and a lower peak at ∼42 Hz was most prominent in SOL. The intensity analysis is a useful tool in exploring postural control and in studying the effects of fatigue on the mechanical properties of skeletal muscle.
Copyright © 2011 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 21708471     DOI: 10.1016/j.jelekin.2011.05.011

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  5 in total

1.  Mechanomyographic parameter extraction methods: an appraisal for clinical applications.

Authors:  Morufu Olusola Ibitoye; Nur Azah Hamzaid; Jorge M Zuniga; Nazirah Hasnan; Ahmad Khairi Abdul Wahab
Journal:  Sensors (Basel)       Date:  2014-12-03       Impact factor: 3.576

2.  Association of anthropometric parameters with amplitude and crosstalk of mechanomyographic signals during forearm flexion, pronation and supination torque tasks.

Authors:  Irsa Talib; Kenneth Sundaraj; Chee Kiang Lam
Journal:  Sci Rep       Date:  2019-11-07       Impact factor: 4.379

Review 3.  Mechanomyogram for muscle function assessment: a review.

Authors:  Md Anamul Islam; Kenneth Sundaraj; R Badlishah Ahmad; Nizam Uddin Ahamed
Journal:  PLoS One       Date:  2013-03-11       Impact factor: 3.240

4.  The Assessment of Muscular Effort, Fatigue, and Physiological Adaptation Using EMG and Wavelet Analysis.

Authors:  Ryan B Graham; Mark P Wachowiak; Brendon J Gurd
Journal:  PLoS One       Date:  2015-08-11       Impact factor: 3.240

5.  Novel pseudo-wavelet function for MMG signal extraction during dynamic fatiguing contractions.

Authors:  Mohammed Rashid Al-Mulla; Francisco Sepulveda
Journal:  Sensors (Basel)       Date:  2014-05-28       Impact factor: 3.576

  5 in total

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