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

  • Mohamed R. Al-Mulla
  • Francisco Sepulveda
  • Mohammad Suoud
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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%.

Keywords

Root Mean Square Linear Discriminant Analysis Joint Angle Muscle Fatigue Biceps Brachii 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mohamed R. Al-Mulla
    • 1
  • Francisco Sepulveda
    • 2
  • Mohammad Suoud
    • 1
  1. 1.College of Computer Science and EngineeringKuwait UniversityKuwait
  2. 2.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK

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