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|Title:||Machine Learning based Electromyography Signal Classification with Optimized Feature Selection for Foot Movements|
|Keywords:||Classification accuracy;EMG;Foot movements;SVM;Time and frequency domain features|
|Abstract:||Electromyography (EMG) signals are bioelectric signals generated by the electrical activities of muscle fibers during contraction or relaxation. Detailed analysis and classification of the complex nature of the signal when related to movements is complicated. However, these are useful for controlling prosthesis and orthosis control systems. In this paper the relevant set of features and the classifier that maps these features to carry out EMG signal classification for four different foot movements is proposed. These movements such as plantar-flexion (PF), dorsi-flexion (DF), inversion (IV) and eversion (EV) are chosen, since these are useful for rehabilitation of persons having a lower limb ankle joint injury which results in gait abnormality. EMG signals are acquired using BIOPAC System (MP 150). The features for EMG signals, in time and frequency domain have been extracted to find optimal features. Further these are classified using support vector machine (SVM), neural network (NN) and logistic regression (LR). From the results, it is depicted that the time domain features reflected better performance. The maximum classification accuracy achieved is 99.69% and average classification accuracy being 94.92 ± 3.03 % using linear SVM for root mean square (RMS) as optimal feature.|
|ISSN:||0975-1084 (Online); 0022-4456 (Print)|
|Appears in Collections:||JSIR Vol.79(11) [November 2020]|
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