Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/55623
Title: Machine Learning based Electromyography Signal Classification with Optimized Feature Selection for Foot Movements
Authors: Khera, Preeti
Kumar, Neelesh
Ahuja, Parul
Keywords: Classification accuracy;EMG;Foot movements;SVM;Time and frequency domain features
Issue Date: Nov-2020
Publisher: NISCAIR-CSIR, India
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.
Page(s): 1011-1016
URI: http://nopr.niscair.res.in/handle/123456789/55623
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.79(11) [November 2020]

Files in This Item:
File Description SizeFormat 
JSIR 79(11) 1011-1016.pdf669.2 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.