Recurrent Neural Network Based Early Prediction of Future Hand Movements

This work focuses on a system for hand prostheses thatcan overcome the delay problem introduced by classical approacheswhile being reliable. The proposed approach based on a recurrentneural network enables us to incorporate the sequential nature ofthe surface electromyogram data and the proposed system can beused 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 revealthat the prediction up to200 msahead in the future is possible withouta significant drop in accuracy. Furthermore, for classification, ourproposed approach outperforms the state of the art classifiers eventhough we used significantly shorter windows for feature extraction.