Paper Info
Title
Optimal Elbow Angle for MMG Signal Classification of Biceps Brachii during Dynamic Fatiguing Contraction.
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%.
Year
Venue
Field
2015
BIOINFORMATICS AND BIOMEDICAL ENGINEERING (IWBBIO 2015), PT I
Computer vision,Biceps,Elbow,Feature extraction,Signal classification,Artificial intelligence,Linear discriminant analysis,Engineering,Muscle fatigue
DocType
Volume
ISSN
Conference
9043
0302-9743
Citations 
PageRank 
References 
2
0.39
5
Authors
3
Name
Order
Citations
PageRank
Mohammed Almulla114720.60
Francisco Sepulveda225226.54
Mohammad Suoud320.39