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http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/19277| Title: | A study of machine learning algorithms for hand gesture classification of sEMG signals Available to Purchase |
| Authors: | Yenuganti, Sujan |
| Keywords: | EEE Instrumentation amplifier Sallen key Machine learning (ML) Feature extraction Confusion matrix |
| Issue Date: | Apr-2025 |
| Publisher: | Emerald |
| 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. |
| URI: | https://www.emerald.com/sr/article-abstract/45/5/699/1256179/A-study-of-machine-learning-algorithms-for-hand?redirectedFrom=fulltext http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/19277 |
| Appears in Collections: | Department of Electrical and Electronics Engineering |
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