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Super Wavelet for sEMG Signal Extraction During Dynamic Fatiguing Contractions

Al-Mulla, MR and Sepulveda, F (2015) 'Super Wavelet for sEMG Signal Extraction During Dynamic Fatiguing Contractions.' Journal of Medical Systems, 39 (1). ISSN 0148-5598

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In this research an algorithm was developed to classify muscle fatigue content from dynamic contractions, by using a genetic algorithm (GA) and a pseudo-wavelet function. Fatiguing dynamic contractions of the biceps brachii were recorded using Surface Electromyography (sEMG) from thirteen subjects. Labelling the signal into two classes (Fatigue and Non-Fatigue) aided in the training and testing phase. The genetic algorithm was used to develop a pseudo-wavelet function that can optimally decompose the sEMG signal and classify the fatigue content of the signal. The evolved pseudo wavelet was tuned using the decomposition of 70 % of the sEMG trials. 28 independent pseudo-wavelet evolution were run, after which the best run was selected and then tested on the remaining 30 % of the trials to measure the classification performance. Results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.45 percentage points to 14.95 percentage points when compared to other standard wavelet functions (p<0.05), giving an average correct classification of 87.90 %.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 08 Jul 2015 14:42
Last Modified: 31 Mar 2021 10:15

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