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. The experiments conducted on the first three Ninapro databases reveal that the prediction up to 200 ms ahead in the future is possible without a significant drop in accuracy. Furthermore, for classification, our proposed approach outperforms the state of the art classifiers even though we used significantly shorter windows for feature extraction.

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.

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. The spatial measurement often requires a huge number of sampling points and entails other difficulties, such as the need for exact calibration of a large number of microphones. In this paper, a method for measuring sound fields using moving microphones is presented. The number of microphones is customizable, allowing for a tradeoff between hardware effort and measurement time. The goal is to reconstruct room impulse responses on a regular grid from data acquired with microphones between grid positions, in general. For this, the sound field at equidistant positions is related to the measurements taken along the microphone trajectories via spatial interpolation. The benefits of using perfect sequences for excitation, a multigrid recovery, and the prospects for reconstruction by compressed sensing are presented.