Semi-Supervised Adaptive Learning for Decoding Movement Intent from Electromyograms

This paper presents an adaptive learning algorithm for predicting movement intent using electromyogram (EMG) signals and controlling a prosthetic arm. The adaptive decoder enables use of the prosthetic systems for long periods of time without the necessity to retrain them. The method of this paper employs a neural network-based decoder and we present a method to update its parameters during the operation phase. Initially, the decoder parameters are estimated during a training phase. During the normal operation, the parameters of the algorithm are updated in a semi-supervised manner based on a movement model. The results presented here, obtained from a single amputee subject, suggest that the approach of this paper improves long-term performance of the decoders over the current state-of-the-art with statistical significance.