IEEE Int Conf Rehabil Robot. 2022 Jul;2022:1-6. doi: 10.1109/ICORR55369.2022.9896414.
Applications of simultaneous and proportional control for upper-limb prostheses typically rely on supervised machine learning to map muscle activations to prosthesis movements. This scheme often poses problems for individuals with limb differences, as they may not be able to reliably reproduce the training activations required to construct a natural motor mapping. We propose an unsupervised myocontrol paradigm that eliminates the need for labeled data by mapping the most salient muscle synergies in arbitrary order to a number of predefined prosthesis actions. The paradigm is coadaptive, in the sense that while the user learns to control the system via interaction, the system continually refines the identification of the user's muscular synergies. Our evaluation consisted of eight subjects without limb-loss performing target achievement control tasks of four actions of the hand and wrist. The subjects achieved comparable performance using the proposed unsupervised myocontrol paradigm and a supervised benchmark method, despite reporting increased mental load with the former.
上肢假肢的同步和比例控制应用通常依赖于监督机器学习来将肌肉活动映射到假肢运动。对于肢体差异的个体来说，这种方案通常会带来问题，因为他们可能无法可靠地再现构建自然运动映射所需的训练活动。我们提出了一种无监督肌控制范式，通过将最显著的肌肉协同作用以任意顺序映射到多个预定义的假肢动作，从而消除了对标记数据的需求。该范式是共适应的，因为在用户通过交互学习控制系统的同时，系统不断改进对用户肌肉协同作用的识别。我们的评估包括 8 名没有肢体缺失的受试者，他们执行了手部和手腕的四个动作的目标达成控制任务。尽管受试者报告前者的精神负荷增加，但他们使用提出的无监督肌控制范式和监督基准方法实现了可比的性能。
