Transfer Learning for Electromyographic Hand Gesture Signal Classification

Abstract

This work performs electromyography-based hand gesture recognition by applying transfer learning on the aggregated data from multiple users while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. The proposed transfer learning scheme outperforms the state-of-the-art, achieving an average accuracy of 98.31% for 7 gestures over 17 participants.