Models and Devices for Coordination Rehabilitation

描述(由申请人提供)：神经系统的损伤经常导致运动控制的改变，导致功能丧失。美国国立卫生研究院估计，与运动相关的神经疾病每年影响数百万美国人。对于许多神经系统疾病，康复治疗是主要的治疗方法。为了优化康复技术，需要一种量化的方法，找出具体的缺陷，并以长期干预为目标。因此，我们将应用生物力学模型、机器人设备和新的控制策略来优化补偿和学习方法，以改进运动控制。这个项目的重点是运动协调性差，这在神经系统受损的人中是普遍存在的。不协调会导致较差的轨迹和靶向控制，并且与小脑功能障碍的关系最为明显。小脑不协调的基本机制将通过比较人类的表现和计算模型，然后通过开发补偿运动障碍或优化依赖练习的学习的机器人控制策略来研究。这两种方法都是必要的，因为对于小脑受损的人来说，短期学习机制可能效率低下或缺乏，这使得补偿成为一些人的最佳选择。机器人外骨骼设备KinArm将被用来获取控制和小脑受试者完成任务期间的行为数据，人类手臂的动态模型将被用来确定控制和小脑数据之间差异的来源。具体地说，受试者的动态模型将被用来模拟肢体动态的错误估计和时间延迟的影响。最能解释行为的参数将被用来为机器人辅助共济失调人群的康复提供合理的控制策略。将设计适应和补偿方法，提供辅助和/或阻力力量，帮助受试者实现正常的运动模式。小脑患者使用这些方法的一项试点研究将为未来康复机器人的发展提供设计指南。这项工作的长期目标是设计和生产定制的带回家的设备，这些设备要么是为了弥补个人的缺陷，要么是为了促进学习新的运动模式。我们计划将这一方法推广到更广泛的患者群体。该项目通过识别机器人可以用来使小脑损伤患者的运动控制正常化的策略，为新型家庭治疗奠定了基础。与公共卫生相关。运动障碍通常发生在神经损伤之后，每年影响数百万美国人的日常生活活动。使用康复机器人的治疗和辅助方法是改善这些患者短期和长期保健的有希望的技术，通过降低治疗成本，实现更有效的基于实践的康复方法，并为无法适应的人群恢复正常运动功能提供“智能”矫形器。

DESCRIPTION (provided by applicant): Damage to the nervous system often results in altered movement control, leading to loss of function. The NIH estimates that movement-related neurological disorders affect millions of Americans each year. For many neurological disorders, rehabilitation therapy is a main treatment. In order to optimize rehabilitation techniques, a quantitative approach that pinpoints specific deficiencies and targets them with long-term intervention is needed. Thus, we will apply biomechanical models, robotic devices and novel control strategies for optimizing both compensation and learning approaches for improved movement control. This project focuses on poor motor coordination, which is a ubiquitous finding in people with damage to the nervous system. Incoordination leads to poor trajectory and targeting control, and is most distinctly related to cerebellar dysfunction. The fundamental mechanism of cerebellar incoordination will be investigated by comparing human performance to computational models, and then by developing robotic control strategies that either compensate for motor impairments or optimize practice-dependent learning. Both approaches are needed since short-term learning mechanisms can be inefficient or absent in individuals with cerebellar damage, making compensation the best option for some people. A robotic exoskeleton device, the KinArm, will be used to acquire behavioral data during reaching tasks performed by control and cerebellar subjects, and dynamic models of the human arm will be used to determine the source of the differences between control and cerebellar data. Specifically, the dynamic models of subjects will be used to simulate the effects of misestimation of limb dynamics and timing delays. The parameters that best explain behavior will be used to inform a rational control strategy for robot-assisted rehabilitation for ataxic populations. Adaptation and compensation methods will be designed that provide assistive and/or resistive forces to help subjects achieve normal movement patterns. A pilot study in which cerebellar patients use these methods will provide design guidelines for future rehabilitation robotics development. The long-term goal of this work is to design and produce take-home devices that are customized to either compensate for an individual's deficit or to facilitate the learning of a new motor pattern. We plan to extend this methodology to a broad range of patient populations. This project lays the foundation for novel home therapies by identifying strategies a robot could use to normalize movement control of people with cerebellar damage. PUBLIC HEALTH RELEVANCE. Movement disorders commonly occur following neurological damage, affecting the activities of daily living for millions of Americans each year. Therapies and assistance methods using rehabilitation robots are promising techniques for improving the short- and long-term health care of these patients, by lowering the cost of treatment, enabling more effective methods for practice-based rehabilitation, and providing "smart" orthoses for recovery of normal movement function in populations for whom adaptation is not possible.