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Improving Control of Dexterous Hand Prostheses Using Adaptive Learning

机译：利用自适应学习改善对手部义齿的控制

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摘要

At the time of this writing, the main means of control for polyarticulated self-powered hand prostheses is surface electromyography (sEMG). In the clinical setting, data collected from two electrodes are used to guide the hand movements selecting among a finite number of postures. Machine learning has been applied in the past to the sEMG signal (not in the clinical setting) with interesting results, which provide more insight on how these data could be used to improve prosthetic functionality. Researchers have mainly concentrated so far on increasing the accuracy of sEMG classification and/or regression, but, in general, a finer control implies a longer training period. A desirable characteristic would be to shorten the time needed by a patient to learn how to use the prosthesis. To this aim, we propose here a general method to reuse past experience, in the form of models synthesized from previous subjects, to boost the adaptivity of the prosthesis. Extensive tests on databases recorded from healthy subjects in controlled and noncontrolled conditions reveal that the method significantly improves the results over the baseline nonadaptive case. This promising approach might be employed to pretrain a prosthesis before shipping it to a patient, leading to a shorter training phase.
机译：在撰写本文时，多关节自供电手假体的主要控制手段是表面肌电图（sEMG）。在临床环境中，从两个电极收集的数据用于指导手部运动在有限数量的姿势中进行选择。过去，机器学习已应用于sEMG信号（不适用于临床环境），并获得了有趣的结果，这些结果为如何使用这些数据改善修复功能提供了更多见识。到目前为止，研究人员主要集中在提高sEMG分类和/或回归的准确性上，但总的来说，更好的控制意味着更长的培训时间。理想的特征是缩短患者学习如何使用假体所需的时间。为此，我们在这里提出一种通用方法，以以前的研究对象合成的模型的形式重用过去的经验，以提高假体的适应性。对健康受试者在受控和非受控条件下记录的数据库进行的广泛测试表明，与基线非自适应案例相比，该方法显着改善了结果。这种有前途的方法可用于在将假体运送给患者之前对假体进行预训练，从而缩短了训练阶段。

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