Recurrent Neural Network Based Early Prediction of Future Hand Movements

This work focuses on a system for hand prostheses that can overcome the delay problem introduced by classical approaches while being reliable. The proposed approach based on a recurrent neural network enables us to incorporate the sequential nature of the surface electromyogram data and the proposed system can be used either for classification or early prediction of hand movements. Especially the latter is a key to a latency free steering of a prosthesis. The experiments conducted on the first three Ninapro databases reveal that the prediction up to 200 ms ahead in the future is possible without a significant drop in accuracy. Furthermore, for classification, our proposed approach outperforms the state of the art classifiers even though we used significantly shorter windows for feature extraction.