A study of machine learning algorithms for hand gesture classification of sEMG signals Available to Purchase

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DC Field	Value	Language
dc.contributor.author	Yenuganti, Sujan	-
dc.date.accessioned	2025-09-01T06:15:34Z	-
dc.date.available	2025-09-01T06:15:34Z	-
dc.date.issued	2025-04	-
dc.identifier.uri	https://www.emerald.com/sr/article-abstract/45/5/699/1256179/A-study-of-machine-learning-algorithms-for-hand?redirectedFrom=fulltext	-
dc.identifier.uri	http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/19277	-
dc.description.abstract	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.	en_US
dc.language.iso	en	en_US
dc.publisher	Emerald	en_US
dc.subject	EEE	en_US
dc.subject	Instrumentation amplifier	en_US
dc.subject	Sallen key	en_US
dc.subject	Machine learning (ML)	en_US
dc.subject	Feature extraction	en_US
dc.subject	Confusion matrix	en_US
dc.title	A study of machine learning algorithms for hand gesture classification of sEMG signals Available to Purchase	en_US
dc.type	Article	en_US
Appears in Collections:	Department of Electrical and Electronics Engineering

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