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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/19278
Title: Semg signal acquisition: of hand movements for feature extraction and classification
Authors: Yenuganti, Sujan
Keywords: EEE
Surface electromyography (sEMG)
Neural network (NN)
Cubic support vector machine (CSVM)
Feature extraction
Issue Date: Apr-2025
Publisher: IEEE
Abstract: This paper explores the classification of surface electromyography (sEMG) signals for hand movement recognition using time and frequency domain features used for feature extraction. Three hand movements were recorded from four healthy subjects for signal analysis achieving an SNR range of 14.81dB to 23.14dB. Two machine learning classifiers, medium neural network (MNN) and cubic support vector machine (CSVM), were evaluated to determine their effectiveness performance. MNN achieved the highest accuracy of 93.8%, demonstrating robust feature separation, while CSVM provided a simpler but slightly less precise result at 92.5%. The findings underscore the potential of MNN in classification of hand movements and aids on the development of advanced prosthetic control systems.
URI: https://ieeexplore.ieee.org/abstract/document/10968604
http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/19278
Appears in Collections:Department of Electrical and Electronics Engineering

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