Department of Electrical and Electronics Engineering

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Author: search.filters.author.Yenuganti, SujanSubject: search.filters.subject.Feature extraction

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    Semg signal acquisition: of hand movements for feature extraction and classification
    (IEEE, 2025-04) Yenuganti, Sujan
    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.
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    A study of machine learning algorithms for hand gesture classification of sEMG signals Available to Purchase
    (Emerald, 2025-04) Yenuganti, Sujan
    This paper presents a cost-effective signal acquisition circuitry (SAC) for capturing surface electromyography (sEMG) data to classify different hand movements using advanced machine learning algorithms. The SAC, comprising an instrumentation amplifier, a Sallen–Key band-pass filter and a noninverting amplifier, is designed and tested on a portable printed circuit board. The purpose of this paper is to perform feature extraction and data segmentation for effective analysis and processing of the recorded sEMG signals.