Effectiveness of Robot-Assisted Hand Movement Training after Stroke

机器人辅助中风后手部运动训练的有效性

• 批准号: 8248743
• 负责人: David Jay Reinkensmeyer
• 金额: $ 28.79万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-03-12 至 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8248743
• 关键词: Algorithms; Anatomy; Behavioral; Biomechanics; Brain imaging; Characteristics; Clinic; Clinical; Clinical assessments; Computer software; Corticospinal Tracts; Cutaneous Muscle; Devices; Diffusion Magnetic Resonance Imaging; Effectiveness; Esthesia; Exercise; Fingers; Functional Imaging; Goals; Hand; Home environment; Ischemic Stroke; Joints; Learning; Libraries; Measures; Modeling; Monitor; Motion; Motor; Motor Activity; Motor output; Movement; Muscle; Outcome; Outcome Measure; Output; Participant; Patients; Persons; Play; Process; Recording of previous events; Recovery; Recruitment Activity; Rehabilitation therapy; Relative (related person); Residual state; Resources; Robot; Robotics; Role; Sensory; Sensory Process; Series; Skin; Stroke; Supratentorial; Technology; Testing; Training; Training Programs; Training Technics; Video Games; Work; base; brain tract; cost; design; experience; grasp; insight; kinematics; post stroke; rehabilitation service; relating to nervous system; research study; response; robot assistance; robotic device; sensor; volunteer

DESCRIPTION (provided by applicant): The broad, long-term, scientific objective of this project is to identify the behavioral and neuroanatomical factors that determine the efficacy of hand movement training after stroke. An important societal impact of achieving this objective will be the design of more effective robotic rehabilitation exercise technology, which will allow people with a stroke to increase their movement recovery beyond that possible with current approaches. The working hypothesis is that robot-assisted movement training following stroke is most effective if it promotes 1) effortful motor output, which produces 2) correlated, appropriate, sensations at the joints, muscles, and skin. Further, it is hypothesized that the amount of hand movement recovery that is possible with such robotic exercise is limited by 1) how much of the main outflow tract from the brain to the hand (i.e. the corticospinal tract, CST) is spared, 2) integrity of sensory processing, and 3) the cumulative history of previous movement practice. These hypotheses will be tested in a series of experiments with a robotic device that assists in grasping objects as the user works daily at home through a library of engaging video games. Aim 1 is to define the benefit of correlated sensory motor activity in robot-assisted movement training. Is it beneficial to receive robot assistance that enhances the sensations experienced during training? A robotic device will help participants to practice grasping movements that they can initiate but normally could not complete, thereby intensifying joint, muscle, and cutaneous sensation. The improvements in motor function caused by this training technique will be compared with improvements when the robot does not help with movement. Aim 2 is to define the benefit of increased motor output levels in robot-assisted movement training. Assisting movements with a robotic device as in Aim 1 can cause people to slack, decreasing their force output. By using advanced robot control software, this aim will test if training with increased relative force levels is more effective. Aim 3 is to identify the effect of patient-specific characteristics on the effectiveness of robot-assisted training. The versions of robot-assisted training to be tested in Aims 1 and 2 presume specific neural resources for optimal effect. Is it possible to predict who will benefit most from different forms of robot-assisted hand exercise? For both Aims 1 and 2, all participants' finger rehabilitation history will be measured starting within 2 weeks of stroke onset using a wearable sensor, and sensory function and the extent of CST damage will be measured when training begins, 3-6 months later. It is hypothesized that availability of CST, sensory function, and history of previous exercise will predict the response to different forms of robotic training.

描述（由申请人提供）：本项目的广泛、长期、科学目标是确定决定中风后手部运动训练疗效的行为和神经解剖学因素。实现这一目标的一个重要社会影响将是设计更有效的机器人康复锻炼技术，这将使中风患者能够在现有方法的基础上增加运动恢复。工作假设是，中风后的机器人辅助运动训练如果能促进1）努力的运动输出，从而在关节、肌肉和皮肤产生2）相关的、适当的感觉，则效果最好。此外，假设利用这种机器人练习可能的手部运动恢复的量受到以下限制：1）从大脑到手部的主要流出道（即皮质脊髓束，CST）的多少被保留，2）感觉处理的完整性，以及3）先前运动实践的累积历史。这些假设将在一系列实验中进行测试，这些实验使用一种机器人设备，当用户每天在家通过一个引人入胜的视频游戏库工作时，该设备可以帮助抓取物体。目的1是确定机器人辅助运动训练中相关感觉运动活动的益处。接受机器人辅助是否有益于增强训练期间的感觉？机器人设备将帮助参与者练习他们可以发起但通常无法完成的抓握运动，从而增强关节，肌肉和皮肤的感觉。这种训练技术所引起的运动功能的改善将与机器人不帮助运动时的改善进行比较。目的2是确定在机器人辅助运动训练中增加运动输出水平的益处。如目标1所示，用机器人设备辅助运动可能会导致人们松懈，从而降低他们的力量输出。通过使用先进的机器人控制软件，这一目标将测试增加相对力量水平的训练是否更有效。目的3是确定患者特定特征对机器人辅助培训有效性的影响。在目标1和目标2中测试的机器人辅助培训版本假定特定的神经资源以获得最佳效果。有没有可能预测谁将从不同形式的机器人辅助手部锻炼中受益最多？对于目标1和目标2，所有参与者的手指康复史将在中风发作后2周内使用可穿戴传感器开始测量，感觉功能和CST损伤程度将在训练开始时测量，3-6个月后。据推测，CST的可用性，感觉功能和以前的运动史将预测不同形式的机器人训练的反应。