Motor learning-based methods offer an alternative paradigm to machine learning-based methods for controlling upper-limb prosthetics. Within this paradigm, the patterns of muscular activity used for control can differ from those which control biological limbs. Practice expedites the learning of these new, functional patterns of muscular activity. We envisage that these methods can result in enhanced control without increasing device complexity. However, key questions about training protocols, generalisation and scalability of motor learning-based methods have remained. In this work, we pursue three objectives: 1) to validate the motor learning-based abstract myoelectric control approach with people with upper-limb difference for the first time; 2) to test whether, after training, participants can generalize their learning to tasks of increased difficulty; and 3) to show that abstract myoelectric control scales with additional input signals, offering a larger control range. In three experiments, 25 limb-intact participants and 8 people with a limb difference (congenital and acquired) experienced a motor learning-based myoelectric controlled interface. We show that participants with upper-limb difference can learn to control the interface and that performance increases with experience. Across experiments, participant performance on easier lower target density tasks generalized to more difficult higher target density tasks. A proof-of-concept study demonstrates that learning-based control scales with additional myoelectric channels. Our results show that human motor learning-based approaches can enhance the number of distinct outputs from the musculature, thereby increasing the functionality of prosthetic hands and providing a viable alternative to machine learning.

中文翻译：

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抽象肌电控制的学习，推广和可扩展性。

基于运动学习的方法为控制上肢假肢的基于机器学习的方法提供了另一种范例。在此范例中，用于控制的肌肉活动模式可能与控制生物肢体的模式不同。实践可加快对这些新的肌肉活动功能模式的学习。我们设想这些方法可以在不增加设备复杂性的情况下增强控制能力。然而，关于训练协议，基于运动学习的方法的通用性和可扩展性的关键问题仍然存在。在这项工作中，我们追求三个目标：1）首次与上肢差异人士验证基于运动学习的抽象肌电控制方法；2）测试训练后是否 参与者可以将他们的学习归纳为难度增加的任务；和3）显示抽象肌电控制与附加输入信号成比例，从而提供更大的控制范围。在三个实验中，有25个肢体完整的参与者和8个肢体不同的人（先天性和后天性）经历了基于运动学习的肌电控制界面。我们表明，上肢差异的参与者可以学习控制界面，并且随着经验的增加，性能会提高。在整个实验中，参与者在较容易实现的较低目标密度任务上的表现被概括为较困难的较高目标密度任务。概念验证研究表明，基于学习的控制与其他肌电通道成比例。