Data from: Electromyography data for non-invasive naturally controlled robotic hand prostheses

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.

康复机器人领域的最新进展表明，手部截肢受试者有望恢复受损手部的大部分功能。当前，采用非侵入式技术控制机器人假手仍是现实场景中的一大挑战：肌电假肢（myoelectric prostheses）的控制能力有限，操作往往不自然，且需要经过长时间训练才能掌握。与此同时，尽管现有科研文献中的研究结果颇具前景，但仍远未满足实际生活中的应用需求。本研究旨在搭建一款基准科学数据集，供全球科研团队开发并测试运动识别与力控算法，以填补这一技术空白。该数据集聚焦于探究表面肌电（surface electromyography）、手部运动学与手部受力之间的关联，最终目标是研发非侵入式、可自然控制的机器人假手。验证部分证实，该数据集的数据与实际场景下采集的数据具有较高相似性，且通过应用前沿信号特征与机器学习算法，可实现对不同手部任务的识别。