ANALYTICAL REVIEW OF METHODS FOR RECORDING AND CLASSIFYING MOVEMENTS BASED ON ELECTROMYOGRAPHY

Abstract

This paper provides a comprehensive overview of optimal methods and processes for recording, processing, and classifying electromyography (EMG) signals in the context of human movement rehabilitation. It begins by exploring advanced techniques for accurate and noise-free EMG signal acquisition, emphasizing the importance of electrode placement, signal amplification, and filtering strategies. The paper then delves into modern signal processing methods, such as feature extraction and dimensionality reduction, which enhance the interpretability of EMG data. Furthermore, the study highlights cutting-edge machine learning and deep learning approaches for classifying movements based on EMG signals, offering insights into their practical applications in rehabilitation systems.