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Sensors 2014, 14(6), 9489-9504; doi:10.3390/s140609489

Novel Pseudo-Wavelet Function for MMG Signal Extraction during Dynamic Fatiguing Contractions

1
College of Computing Science and Engineering, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait
2
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
*
Author to whom correspondence should be addressed.
Received: 25 March 2014 / Revised: 1 May 2014 / Accepted: 19 May 2014 / Published: 28 May 2014
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Abstract

The purpose of this study was to develop an algorithm to classify muscle fatigue content in sports related scenarios. Mechanomyography (MMG) signals of the biceps muscle were recorded from thirteen subjects performing dynamic contractions until fatigue. For training and testing purposes, the signals were labeled in two classes (Non-Fatigue and Fatigue). A genetic algorithm was used to evolve a pseudo-wavelet function for optimizing the detection of muscle fatigue. Tuning of the generalized evolved pseudo-wavelet function was based on the decomposition of 70% of the conducted MMG trials. After completing 25 independent pseudo-wavelet evolution runs, the best run was selected and then tested on the remaining 30% of the data to measure the classification performance. Results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.70 percentage points to 16.61 percentage points when compared to other standard wavelet functions, giving an average correct classification of 80.63%, with statistical significance (p < 0:05). View Full-Text
Keywords: genetic algorithms; localised muscle fatigue; mechanomyography; wavelet analysis; pseudo-wavelets genetic algorithms; localised muscle fatigue; mechanomyography; wavelet analysis; pseudo-wavelets
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Al-Mulla, M.R.; Sepulveda, F. Novel Pseudo-Wavelet Function for MMG Signal Extraction during Dynamic Fatiguing Contractions. Sensors 2014, 14, 9489-9504.

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