2003•IEEE Transactions on Biomedical EngineeringRequires access

Fatigue analysis of the surface EMG signal in isometric constant force contractions using the averaged instantaneous frequency

Apostolos Georgakis, Lampros K. Stergioulas, Giannis Giakas

Open publisher page 161 citations

Abstract

The averaged instantaneous frequency (AIF) is proposed as an alternative method for the frequency analysis of surface electromyography (EMG) in the study of muscle fatigue during sustained, isometric muscle contractions. Results from performance analysis using experimental EMG signals demonstrate the low variability of the proposed frequency variable. Indeed, the AIF measure is shown to perform significantly better than the widely used mean and median frequency variables, in terms of robustness to the length of the analysis window.

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What this paper is about

The averaged instantaneous frequency (AIF) is proposed as an alternative method for the frequency analysis of surface electromyography (EMG) in the study of muscle fatigue during sustained, isometric muscle contractions. Results from performance analysis using experimental EMG signals demonstrate the low variability of the proposed frequency variable. Indeed, the AIF measure is shown to perform significantly better than the widely used mean and median frequency variables, in terms of robustness to the length of the analysis window.

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Available abstract

The averaged instantaneous frequency (AIF) is proposed as an alternative method for the frequency analysis of surface electromyography (EMG) in the study of muscle fatigue during sustained, isometric muscle contractions. Results from performance analysis using experimental EMG signals demonstrate the low variability of the proposed frequency variable. Indeed, the AIF measure is shown to perform significantly better than the widely used mean and median frequency variables, in terms of robustness to the length of the analysis window.

Key concepts: Isometric exercise, Electromyography, Frequency analysis, Robustness (evolution), Time–frequency analysis, Muscle fatigue, SIGNAL (programming language), Signal processing

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