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International Conference on Bioinformatics and Biomedical Engineering

IWBBIO 2015: Bioinformatics and Biomedical Engineering pp 303–314Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9043))

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%.

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Authors and Affiliations

  1. College of Computer Science and Engineering, Kuwait University, Kuwait

    Mohamed R. Al-Mulla & Mohammad Suoud

  2. School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK

    Francisco Sepulveda

Authors
  1. Mohamed R. Al-Mulla
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  2. Francisco Sepulveda
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  3. Mohammad Suoud
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Editor information

Editors and Affiliations

  1. Dpto. de Arquitectura y Tecnología de Computadores (ATC)., E.T.S. de Ingenierías en Informática y Telecomunicación. CITIC-UGR, Universidad de Granada, c/ Periodista Daniel Saucedo Aranda s/n, 18071, Granada, Spain

    Francisco Ortuño

  2. E.T.S. Ingenierías Informática y de Telecomunicación , , Dpto. Arquitectura y Tecnología de Computadores, CITIC-UGR, Universidad de Granada, C Periodista Rafael Gómez Montero, 18071, Granada, Spain

    Ignacio Rojas

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Al-Mulla, M.R., Sepulveda, F., Suoud, M. (2015). Optimal Elbow Angle for MMG Signal Classification of Biceps Brachii during Dynamic Fatiguing Contraction. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2015. Lecture Notes in Computer Science(), vol 9043. Springer, Cham. https://doi.org/10.1007/978-3-319-16483-0_31

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  • DOI: https://doi.org/10.1007/978-3-319-16483-0_31

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16482-3

  • Online ISBN: 978-3-319-16483-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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