Efficient strategies for finger movement classification using surface electromyogram signals.

Autor:	Prabhakar SK; Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, Republic of Korea., Won DO; Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, Republic of Korea.
Jazyk:	angličtina
Zdroj:	Frontiers in neuroscience [Front Neurosci] 2023 Jun 22; Vol. 17, pp. 1168112. Date of Electronic Publication: 2023 Jun 22 (Print Publication: 2023).
DOI:	10.3389/fnins.2023.1168112
Abstrakt:	One of the famous research areas in biomedical engineering and pattern recognition is finger movement classification. For hand and finger gesture recognition, the most widely used signals are the surface electromyogram (sEMG) signals. With the help of sEMG signals, four proposed techniques of finger movement classification are presented in this work. The first technique proposed is a dynamic graph construction and graph entropy-based classification of sEMG signals. The second technique proposed encompasses the ideas of dimensionality reduction utilizing local tangent space alignment (LTSA) and local linear co-ordination (LLC) with evolutionary algorithms (EA), Bayesian belief networks (BBN), extreme learning machines (ELM), and a hybrid model called EA-BBN-ELM was developed for the classification of sEMG signals. The third technique proposed utilizes the ideas of differential entropy (DE), higher-order fuzzy cognitive maps (HFCM), empirical wavelet transformation (EWT), and another hybrid model with DE-FCM-EWT and machine learning classifiers was developed for the classification of sEMG signals. The fourth technique proposed uses the ideas of local mean decomposition (LMD) and fuzzy C-means clustering along with a combined kernel least squares support vector machine (LS-SVM) classifier. The best classification accuracy results (of 98.5%) were obtained using the LMD-fuzzy C-means clustering technique classified with a combined kernel LS-SVM model. The second-best classification accuracy (of 98.21%) was obtained using the DE-FCM-EWT hybrid model with SVM classifier. The third best classification accuracy (of 97.57%) was obtained using the LTSA-based EA-BBN-ELM model. Competing Interests: The author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Prabhakar and Won.)
Databáze:	MEDLINE
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