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Evolved pseudo-wavelet function to optimally decompose sEMG for automated classification of localized muscle fatigue

Al-Mulla, Mohamed R and Sepulveda, Francisco and Colley, M (2011) 'Evolved pseudo-wavelet function to optimally decompose sEMG for automated classification of localized muscle fatigue.' Medical Engineering and Physics, 33 (4). pp. 411-417. ISSN 1350-4533

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The purpose of this study was to develop an algorithm for automated muscle fatigue detection in sports related scenarios. Surface electromyography (sEMG) of the biceps muscle was recorded from ten subjects performing semi-isometric (i.e., attempted isometric) contraction until fatigue. For training and testing purposes, the signals were labelled in two classes (Non-Fatigue and Fatigue), with the labelling being determined by a fuzzy classifier using elbow angle and its standard deviation as inputs. A genetic algorithm was used for evolving a pseudo-wavelet function for optimising the detection of muscle fatigue on any unseen sEMG signals. Tuning of the generalised evolved pseudo-wavelet function was based on the decomposition of twenty sEMG trials. After completing twenty independent pseudo-wavelet evolution runs, the best run was selected and then tested on ten previously unseen sEMG trials to measure the classification performance. Results show that an evolved pseudo-wavelet improved the classification of muscle fatigue between 7.31% and 13.15% when compared to other wavelet functions, giving an average correct classification of 88.41%. © 2010 IPEM.

Item Type: Article
Uncontrolled Keywords: Genetic algorithms; Localized muscle fatigue; sEMG; Wavelet analysis
Subjects: R Medicine > R Medicine (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 14 Aug 2012 11:48
Last Modified: 23 Sep 2022 19:05

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