Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Top 10 algorithms in data mining
Knowledge and Information Systems
Robust EMG pattern recognition to muscular fatigue effect for powered wheelchair control
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Theoretical advances of intelligent paradigms
Feature reduction and selection for EMG signal classification
Expert Systems with Applications: An International Journal
EMG feature evaluation for improving myoelectric pattern recognition robustness
Expert Systems with Applications: An International Journal
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In this work, an attempt has been made to differentiate surface electromyography (sEMG) signals under muscle fatigue and non-fatigue conditions with multiple time window (MTW) features. sEMG signals are recorded from biceps brachii muscles of 50 volunteers. Eleven MTW features are extracted from the acquired signals using four window functions, namely rectangular windows, Hamming windows, trapezoidal windows, and Slepian windows. Prominent features are selected using genetic algorithm and information gain based ranking. Four different classification algorithms, namely naive Bayes, support vector machines, k-nearest neighbour, and linear discriminant analysis, are used for the study. Classifier performances with the MTW features are compared with the currently used time- and frequency-domain features. The results show a reduction in mean and median frequencies of the signals under fatigue. Mean and variance of the features differ by an order of magnitude between the two cases considered. The number of features is reduced by 45% with the genetic algorithm and 36% with information gain based ranking. The k-nearest neighbour algorithm is found to be the most accurate in classifying the features, with a maximum accuracy of 93% with the features selected using information gain ranking.