Learning a Hand Model from Dynamic Movements Using High-Density EMG and Convolutional Neural Networks
Deep learning model decodes surface electromyographic signals into proportional hand movements, accurately controlling both individual finger and compound movements, with potential to enhance intuitive interfaces for assistive hand devices.
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