WSEAS Transactions on Signal Processing
Print ISSN: 1790-5052, E-ISSN: 2224-3488
Volume 18, 2022
Time Domain Analysis of EMG Signals using KNN and SVM Techniques
Authors: Prakash M. B., Harish H. M., Niranjana Kumara M.
Abstract: The EMG signals that have been processed can mimic human movements. For this study, raw EMG data obtained when the hands are in repose (rest), in a clasp, and when the wrist is buckled and stretched were used to categorise four distinct forms of hand gestures using a MATLAB-based intelligent framework (open access data set). Statistical-time-domain features are applied to sort various hand gestures in this investigation. The K-Nearest-Neighbor (KNN) and Support-Vector-Machine (SVM) classifiers are used for classification and comparison. Furthermore, our method outperforms a state-of-the-art method on other data sets of hand gestures.
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Keywords: Hand gesture recognition, Support-Vector-Machine, K-Nearest-Neighbour, Electromyography, Empirical Mode Decomposition, Kaggle Database
Pages: 70-76
DOI: 10.37394/232014.2022.18.10
WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 18, 2022, Art. #10