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Real-time computer modeling of weakness following stroke optimizes robotic assistance for movement therapy

机译：卒中后无力的实时计算机建模可优化用于运动治疗的机器人辅助

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This paper describes the development of a novel control system for a robotic arm orthosis for assisting patients in motor training following stroke. The robot allows naturalistic motion of the arm and is as mechanically compliant as a human therapist''s arms. This compliance preserves the connection between effort and error that appears essential for motor learning, but presents a challenge: accurately creating desired movements requires that the robot form a model of the patient''s weakness, since the robot cannot simply stiffly drive the arm along the desired path. We show here that a standard model-based adaptive controller allows the robot to form such a model of the patient and complete movements accurately. However, we found that the human motor system, when coupled to such an adaptive controller, reduces its own participation, allowing the adaptive controller to take over the performance of the task. This presents a problem for motor training, since active engagement by the patient is important for stimulating neuroplasticity. We show that this problem can be solved by making the controller continuously attempt to reduce its assistance when errors are small. The resulting robot successfully assists stroke patients in moving in desired patterns with very small errors, but also encourages intense participation by the patient. Such robot assistance may optimally provoke neural plasticity, since it intensely engages both descending and ascending motor pathways.
机译：本文介绍了一种新型的机器人手臂矫形器控制系统的开发，该系统可帮助患者中风后进行运动训练。该机器人允许手臂自然运动，并且在机械上与人类治疗师的手臂一样柔顺。这种顺从性保留了努力与错误之间的联系，这似乎是运动学习所必不可少的，但也带来了挑战：要准确地创建所需的动作，就需要机器人形成患者虚弱的模型，因为机器人无法简单地沿手臂僵硬地驱动手臂所需的路径。我们在这里展示了一个基于标准模型的自适应控制器，该机器人可以使机器人形成患者的这种模型并准确地完成运动。但是，我们发现人机系统与这种自适应控制器耦合后，会减少自身的参与程度，从而使自适应控制器可以接管任务的执行。这给运动训练带来了问题，因为患者的主动参与对于刺激神经可塑性很重要。我们表明，可以通过使控制器在误差较小时连续尝试减少其帮助来解决此问题。最终的机器人成功地帮助中风患者以很小的误差以期望的模式运动，但是也鼓励了患者的强烈参与。这样的机器人辅助可以最佳地激发神经可塑性，因为它强烈地参与了运动路径的下降和上升。

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