Models and Devices for Coordination Rehabilitation

协调康复模型和设备

• 批准号: 7531545
• 负责人: Allison Mariko Okamura
• 金额: $ 21.95万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2010-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7531545
• 关键词: Abnormal coordination; Activities of Daily Living; Address; Affect; American; Back; Behavior; Behavioral; Biomechanics; Biomedical Engineering; Categories; Cerebellar Ataxia; Cerebellar Diseases; Characteristics; Computer Simulation; Condition; Data; Deceleration; Development; Devices; Educational process of instructing; Engineering; Financial compensation; Foundations; Functional disorder; Future; Goals; Guidelines; Healthcare; Home environment; Human; Individual; Intervention; Joints; Laws; Learning; Limb structure; Mechanics; Methodology; Methods; Modeling; Motion; Motor; Movement; Movement Disorders; Muscle; Nervous System Trauma; Nervous system structure; Neuronal Plasticity; Neurosciences; Orthotic Devices; Patient Care; Patients; Pattern; Performance; Pilot Projects; Population; Public Health; Range; Recovery; Rehabilitation therapy; Research Project Grants; Risk; Robot; Robotics; Simulate; Source; Techniques; Testing; Therapeutic; Time; Torque; Training; Treatment Cost; United States National Institutes of Health; Upper arm; Work; base; design; exoskeleton; falls; improved; innovation; loss of function; motor impairment; motor learning; nervous system disorder; novel; novel strategies; research study; robot assistance; robotic device; simulation; theories

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.

描述（由申请人提供）：神经系统损伤常导致运动控制改变，导致功能丧失。美国国立卫生研究院估计，与运动相关的神经系统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，需要一种定量的方法来确定具体的缺陷并针对它们进行长期干预。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能力差，这在神经系统受损的人群中是普遍存在的。不协调导致运动轨迹和目标控制不良，与小脑功能障碍最明显相关。小脑不协调的基本机制将通过将人类表现与计算模型进行比较来研究，然后通过开发机器人控制策略来补偿运动障碍或优化练习依赖学习。这两种方法都是必要的，因为短期学习机制在小脑损伤的个体中可能效率低下或缺乏，因此补偿对某些人来说是最好的选择。机器人外骨骼设备KinArm将用于获取控制和小脑受试者在完成任务时的行为数据，而人类手臂的动态模型将用于确定控制和小脑数据之间差异的来源。具体来说，受试者的动力学模型将用于模拟肢体动力学和时间延迟的错误估计的影响。最能解释行为的参数将用于为共济失调人群的机器人辅助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，以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初步研究将为未来康复机器人的发展提供设计指导。这项工作的长期目标是设计和生产定制的带回家的设备，以补偿个人的缺陷或促进新的运动模式的学习。我们计划将这种方法扩展到更广泛的患者群体。该项目通过确定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，为新型家庭疗法奠定了基础。公共卫生相关性。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有前途的技术，可以通过降低治疗成本，实现更有效的基于实践的康复方法，并为无法适应的人群提供恢复正常运动功能的“智能”矫形器，从而改善这些患者的短期和长期医疗保健。