DOI: 10.5061/dryad.1k84r
title: Data from: Electromyography data for non-invasive naturally controlled robotic hand prostheses

description: Recent advances in rehabilitation robotics suggest that it may be possible
 for hand-amputated subjects to recover at least a significant part of the
 lost hand functionality. The control of robotic prosthetic hands using
 non-invasive techniques is still a challenge in real life: myoelectric
 prostheses give limited control capabilities, the control is often
 unnatural and must be learned through long training times. Meanwhile,
 scientific literature results are promising but they are still far from
 fulfilling real-life needs. This work aims to close this gap by allowing
 worldwide research groups to develop and test movement recognition and
 force control algorithms on a benchmark scientific database. The database
 is targeted at studying the relationship between surface electromyography,
 hand kinematics and hand forces, with the final goal of developing
 non-invasive, naturally controlled, robotic hand prostheses. The
 validation section verifies that the data are similar to data acquired in
 real-life conditions, and that recognition of different hand tasks by
 applying state-of-the-art signal features and machine-learning algorithms
 is possible.
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