IEEE Trans Neural Syst Rehabil Eng. 2022;30:610-620. doi: 10.1109/TNSRE.2022.3157710. Epub 2022 Mar 21.
We aim to develop a paradigm for simultaneous and independent control of multiple degrees of freedom (DOFs) for upper-limb prostheses. To that end, we introduce action control, a novel method to operate prosthetic digits with surface electromyography (EMG) based on multi-output, multi-class classification. At each time step, the decoder classifies movement intent for each controllable DOF into one of three categories: open, close, or stall (i.e., no movement). We implemented a real-time myoelectric control system using this method and evaluated it by running experiments with one unilateral and two bilateral amputees. Participants controlled a six-DOF bar interface on a computer display, with each DOF corresponding to a motor function available in multi-articulated prostheses. We show that action control can significantly and systematically outperform the state-of-the-art method of position control via multi-output regression in both task- and non-task-related measures. Using the action control paradigm, improvements in median task performance over regression-based control ranged from 20.14% to 62.32% for individual participants. Analysis of a post-experimental survey revealed that all participants rated action higher than position control in a series of qualitative questions and expressed an overall preference for the former. Action control has the potential to improve the dexterity of upper-limb prostheses. In comparison with regression-based systems, it only requires discrete instead of real-valued ground truth labels, typically collected with motion tracking systems. This feature makes the system both practical in a clinical setting and also suitable for bilateral amputation. This work is the first demonstration of myoelectric digit control in bilateral upper-limb amputees. Further investigation and pre-clinical evaluation are required to assess the translational potential of the method.
我们旨在为上肢假肢开发一种同时且独立控制多个自由度（DOF）的范例。为此，我们引入了动作控制，这是一种基于多输出、多类分类操作假肢手指的新型表面肌电（EMG）方法。在每个时间步，解码器将每个可控制 DOF 的运动意图分类为三个类别之一：张开、闭合或失速（即无运动）。我们使用此方法实现了实时肌电控制系统，并通过与一名单侧和两名双侧截肢者进行实验评估。参与者在计算机显示器上控制一个具有六个自由度的棒状界面，每个 DOF 对应于多关节假肢中的一种可用的运动功能。我们表明，在任务和非任务相关的测量中，动作控制都可以显著且系统地优于基于多输出回归的位置控制方法。对于个体参与者，与基于回归的控制相比，使用动作控制范例在任务性能上的中位数改进范围从 20.14%到 62.32%。对实验后调查的分析表明，所有参与者在一系列定性问题中都对动作控制的评分高于位置控制，并总体上表示对前者的偏好。动作控制有可能提高上肢假肢的灵活性。与基于回归的系统相比，它仅需要离散而不是实际值的地面真实标签，通常使用运动跟踪系统收集。此功能使系统既在临床环境中实用，也适合双侧截肢。这是在双侧上肢截肢者中进行肌电手指控制的首次演示。需要进一步的研究和临床前评估来评估该方法的转化潜力。
