Interface prostheses with classifier-feedback based user training

It is evident that user training significantly affects performance of pattern-recognition based myoelectric prosthetic device control. Despite plausible classification accuracy on off-line datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent EMG patterns can be enhanced via proper user training strategies in order to improve online performance.

This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualised online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering-feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback based user training, while conventional classifier-feedback methods, i.e. label-feedback, hardly achieve any improvement. The result concludes that the use of proper classifier-feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition based prosthetic device manipulation.