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.

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.

Sound-field measurement with moving microphones.

Closed-room scenarios are characterized by reverberation, which decreases the performance of applications such as hands-free teleconferencing and multichannel sound reproduction. However, exact knowledge of the sound field inside a volume of interest enables the compensation of room effects and allows for a performance improvement within a wide range of applications. The sampling of sound fields involves the measurement of spatially dependent room impulse responses, where the Nyquist-Shannon sampling theorem applies in the temporal and spatial domains.