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Optimal Elbow Angle for MMG Signal Classification of Biceps Brachii during Dynamic Fatiguing Contraction

Al-Mulla, MR and Sepulveda, F and Suoud, M (2015) 'Optimal Elbow Angle for MMG Signal Classification of Biceps Brachii during Dynamic Fatiguing Contraction.' In: Ortu�o, F and Rojas, I, (eds.) Bioinformatics and Biomedical Engineering. Lecture Notes in Computer Science . Springer, 303 - 314.

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Abstract

Mechanomyography (MMG) activity of the biceps muscle was recorded from thirteen subjects. Data was recorded while subjects performed dynamic contraction until fatigue. The signals were segmented into two parts (Non-Fatigue and Fatigue), An evolutionary algorithm was used to determine the elbow angles that best separate (using DBi) both Non-Fatigue and Fatigue segments of the MMG signal. Establishing the optimal elbow angle for feature extraction used in the evolutionary process was based on 70% of the conducted MMG trials. After completing twenty-six independent evolution runs, the best run containing the best elbow angles for separation (fatigue and non-fatigue) was selected and then tested on the remaining 30% of the data to measure the classification performance. Testing the performance of the optimal angle was undertaken on eight features that where extracted from each of the two classes (non-fatigue and fatigue) to quantify the performance. Results show that the elbow angles produced by the Genetic algorithm can be used for classification showing 80.64% highest correct classification for one of the features and on average of all eight features including worst performing features giving 66.50%.

Item Type: Book Section
Additional Information: Third International Conference, IWBBIO 2015, Granada, Spain, April 15-17, 2015, Proceedings, Part I
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QH Natural history > QH301 Biology
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 25 Aug 2015 10:37
Last Modified: 17 Aug 2017 17:34
URI: http://repository.essex.ac.uk/id/eprint/14604

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