Early prediction of future hand movements using sEMG data.

We study in this work the feasibility of early prediction of hand movement based on sEMG signals to overcome the time delay issue of the conventional classification. Opposed to the classification task, the objective of early prediction is to predict a hand movement that is going to occur in the future given the information up to the current time point. The ability of early prediction may allow a hand prosthesis control system to compensate for the time delay and, as a result, improve the usability. Experimental results on the Ninapro database show that we can predict up to 300 ms ahead in the future while the prediction accuracy remains very close to that of the standard classification, i.e. it is just marginally lower. Furthermore, historical data prior the current time window is shown to be very important to improve performance, not only for the prediction but also the classification task.