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

Al-Mulla, M and Sepulveda, F (2014) 'Novel Pseudo-Wavelet Function for MMG Signal Extraction during Dynamic Fatiguing Contractions.' Sensors, 14 (6). 9489 - 9504. ISSN 1424-8220

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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).

Item Type: Article
Uncontrolled Keywords: genetic algorithms; localised muscle fatigue; mechanomyography; wavelet analysis; pseudo-wavelets
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 05 Dec 2014 12:45
Last Modified: 17 Aug 2017 17:43
URI: http://repository.essex.ac.uk/id/eprint/12005

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