Upper Limb EMG

This study explores the potential of electromyography (EMG) decoding to enhance motor function outcomes, focusing on developing an innovative EMG-based hand movement classification system. Leveraging advanced signal processing and machine learning techniques, our objectives are twofold: (1) optimize EMG decoding performance through time-domain windowing, feature selection, and classifier optimization, and (2) assess the system's effectiveness in classifying 15 distinct finger movements. With a validation accuracy of 99.98%, this research contributes to the development of more accurate myoelectric control systems, improving rehabilitation outcomes and quality of life for individuals with neurological disorders.