CLASSIFICATION OF EMG SIGNALS USING ARTIFICIAL INTELLIGENCE
Keywords:
artificial intelligence, EMG signals, electromyography, signal classification, machine learning, deep learning, biomedical engineering, feature extraction, prosthetic control.Abstract
This article discusses the classification of electromyography signals using artificial intelligence methods. Electromyography signals reflect the electrical activity of muscles and are widely used in biomedical engineering, rehabilitation, prosthetic control, human-computer interaction, and clinical diagnostics. The classification of EMG signals is a complex task because these signals are nonlinear, noisy, and highly dependent on individual physiological characteristics. Artificial intelligence methods, including machine learning and deep learning, provide effective tools for extracting features, recognizing muscle activity patterns, and improving classification accuracy. The article analyzes the main stages of EMG signal processing, feature extraction, AI-based classification methods, applications, challenges, and future prospects.
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