EFFECTIVENESS OF ROBOT-ASSISTED HAND MOVEMENT TRAINING AFTER STROKE

中风后机器人辅助手部运动训练的有效性

• 批准号: 10643069
• 负责人: David Jay Reinkensmeyer
• 金额: $ 13.4万
• 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 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10643069
• 关键词: Anatomy; Behavioral; Behavioral Mechanisms; Clinical; Devices; Effectiveness; Elements; Fingers; Goals; Grant; Hand; Hand functions; Impairment; Individual; Injury; Ischemic Stroke; Learning; Lesion; Life; Magnetic Resonance Imaging; Measures; Modeling; Motor; Movement; Music; Participant; Patient Recruitments; Patients; Persons; Play; Population; Proprioception; Protocols documentation; Randomized; Recovery; Rehabilitation therapy; Resources; Rewards; Robot; Robotics; Role; Sensory; Series; Stroke; Structure; Supratentorial; System; Technology; Testing; Training; Work; base; behavior measurement; computer game; design; exercise rehabilitation; experience; experimental study; finger movement; functional magnetic resonance imaging/electroencephalography; grasp; improved; innovation; insight; mathematical model; movement practice; neuromechanism; novel; post stroke; predicting response; recruit; relating to nervous system; response; robot assistance; robot rehabilitation; robot therapy; robotic device; robotic training; sensor; somatosensory; stroke rehabilitation; success; volunteer; wearable sensor technology

Project Summary/Abstract The broad, long-term, scientific objective of this project is to identify the behavioral and neural factors that determine the efficacy of robotic 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. In our previous grant, we found that participants with impaired finger proprioception (quantified robotically with a novel protocol) did not achieve a functional benefit from robotic finger training. Reduced benefit also correlated with injury to and abnormal activation of the somatosensory system. Our working hypothesis is that finger proprioceptive integrity is a gateway for robotic assistance because it allows such assistance to stimulate a Hebbian-like learning mechanism. Building on this “Hebbian Hypothesis”, we add two other hypotheses: improving proprioceptive capacity with targeted training for those with impaired proprioception will enhance the effectiveness of subsequent robotic finger training, and proprioceptive integrity modulates spontaneous self- training outside of formal training. We will test these hypotheses in a series of experiments with a novel robotic finger training device (“FINGER”) that assists participants in making different grips to play notes in a musical computer game similar to Guitar Hero. We will recruit participants with hand movement deficits at least six months after stroke and randomize the participants with intact finger proprioception to participate in Aim 1, and those with impaired finger proprioception to participate in Aim 2. Aim 1 is to identify the magnitude of the Hebbian benefit from robot-assisted movement training. Robotic assistance enhances proprioceptive input, but it typically also enhances reward, because it increases task success. We will determine the magnitude of the benefit of the enhanced proprioceptive input, beyond the benefit due to enhanced reward. Aim 2 is to determine the extent to which finger proprioception can be improved through targeted robotic proprioceptive training, and thereby enhance response to subsequent robotic finger movement training. Aim 3 is to identify mathematical models that predict the response to the different forms of training experienced by the participants in Aim 1 and 2. The model inputs will be baseline behavioral and demographic measures, lesion overlap with sensory and motor structures (via anatomical MRI), and activation of sensory-motor networks (via fMRI and EEG). We aim to provide further insight into the high variance in robotic training response, and determine who responds best to proprioceptive training. Aim 4 is to identify the role of impaired proprioception in decreasing spontaneous hand use in daily life using a novel wearable sensor, because hand use outside of robotic therapy sessions likely exerts a powerful training effect. Testing of these hypotheses will provide insight into how to manipulate the unique resources for sensory motor plasticity that each patient retains using robot-based movement training.

项目总结/摘要 这个项目的广泛的、长期的、科学的目标是识别出 确定中风后机器人手部运动训练的有效性。一个重要的社会影响， 这一目标将是设计更有效的机器人康复训练技术， 中风患者，以增加他们的运动恢复超过目前的方法可能。在我们 在之前的研究中，我们发现手指本体感觉受损的参与者（用一种新的机器人量化）， 方案）没有从机器人手指训练中获得功能益处。收益减少也与 躯体感觉系统的损伤和异常激活。我们的假设是 本体感受完整性是机器人辅助的门户，因为它允许这种辅助刺激神经元， Hebbian学习机制在这个“赫布假说”的基础上，我们增加了另外两个假设： 通过针对性的训练来提高本体感受能力， 随后的机器人手指训练的有效性和本体感受完整性调节自发的自我 在正规培训之外。我们将在一系列实验中测试这些假设， 手指训练装置（"手指"），其帮助参与者进行不同的抓握以演奏音乐剧中的音符 游戏名称：Guitar Hero我们将招募手部运动缺陷至少6 中风后3个月，随机分配手指本体感觉完整的参与者参加目标1， 手指本体感觉受损的人参与目标2。目标1是确定赫布星系的大小 受益于机器人辅助运动训练。机器人辅助增强了本体感受输入，但它通常 也会提高奖励，因为它增加了任务的成功率。我们将确定的好处的大小， 增强的本体感受输入，超出了由于增强的奖励而带来的益处。目标2是确定 该手指本体感受可以通过有针对性的机器人本体感受训练来改善， 增强对后续机器人手指运动训练的反应。目标3是确定数学模型， 预测目标1和目标2中参与者对不同形式培训的反应。模型 输入将是基线行为和人口统计学测量，与感觉和运动结构重叠的病变 (via解剖MRI）和感觉运动网络的激活（通过fMRI和EEG）。我们的目标是进一步提供 深入了解机器人训练反应的高差异，并确定谁对本体感受反应最好 训练目的4是确定本体感觉受损在减少日常生活中自发用手中的作用 使用一种新型的可穿戴传感器，因为在机器人治疗会话之外使用手可能会产生强大的 培训效果这些假设的测试将提供洞察如何操纵独特的资源， 感觉运动可塑性，每个病人都使用基于机器人的运动训练来保持。

项目成果

Effect of visual distraction and auditory feedback on patient effort during robot-assisted movement training after stroke.

• DOI: 10.1186/1743-0003-8-21
• 发表时间: 2011-04-23
• 期刊: Journal of neuroengineering and rehabilitation
• 影响因子: 5.1
• 作者: Secoli R;Milot MH;Rosati G;Reinkensmeyer DJ
• 通讯作者: Reinkensmeyer DJ

Smart calibration for video game play by people with a movement impairment.

针对运动障碍人士玩视频游戏的智能校准。

• DOI: 10.1109/iembs.2011.6091663
• 发表时间: 2011
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Perez,Sergi;Benitez,Raul;Reinkensmeyer,DavidJ
• 通讯作者: Reinkensmeyer,DavidJ

Adaptive control with state-dependent modeling of patient impairment for robotic movement therapy.

• DOI: 10.1109/icorr.2013.6650460
• 发表时间: 2013-06
• 期刊: IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
• 作者: Bower C;Taheri H;Wolbrecht E
• 通讯作者: Wolbrecht E

Robotically quantifying finger and ankle proprioception: Role of range, speed, anticipatory errors, and learning.

机器人量化手指和脚踝本体感觉：范围、速度、预期错误和学习的作用。

• DOI: 10.1109/embc40787.2023.10340566
• 发表时间: 2023
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Johnson,ChristopherA;Reinsdorf,DylanS;Reinkensmeyer,DavidJ;Farrens,AndriaJ
• 通讯作者: Farrens,AndriaJ

Design and preliminary evaluation of the FINGER rehabilitation robot: controlling challenge and quantifying finger individuation during musical computer game play.

• DOI: 10.1186/1743-0003-11-10
• 发表时间: 2014-02-04
• 期刊: Journal of neuroengineering and rehabilitation
• 影响因子: 5.1
• 作者: Taheri H;Rowe JB;Gardner D;Chan V;Gray K;Bower C;Reinkensmeyer DJ;Wolbrecht ET
• 通讯作者: Wolbrecht ET