Classification of hand gesture based on optimal deep feature selection using sEMG signal

Key Points

• Hand gesture classification achieved over 95% accuracy with relevant sEMG signal features.
• Key evidence shows deep learning improves classification performance, optimizing feature selection.
• Analysis employed machine learning algorithms to enhance classification from surface electromyography data.
• Study findings support the utility of deep feature selection, with implications for prosthetics and human-computer interaction.