Automated hand gesture recognition from multichannel surface electromyography signals through ML and DL models
DOI:
https://doi.org/10.83179/th8hpj19Keywords:
surface electromyography, hand gesture recognition, machine learning, deep learningAbstract
Hand gesture recognition (HGR) based on surface electromyography (sEMG) signals has established itself as a line of research of great interest in the biomedical and technological fields, due to its potential application in prosthetics, rehabilitation, and the control of smart devices. This review analyzes recent advances and technical challenges associated with HGR, considering the typical flow of HGR systems: data acquisition, preprocessing, feature extraction, and classification. Traditional machine learning (ML) techniques such as Support Vector Machine (SVM), k-nearest neighbors (KNN), and Random Forests are addressed, as well as deep learning (DL) approaches, including Convolutional Neural Network (CNN) and hybrid models. It also discusses the main challenges in the field, including inter-subject variability, the lack of labeled databases, and the robustness of models in real-world environments. This review identifies research gaps and future directions for developing more robust, accessible, and generalizable HGR systems.
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