Effectiveness of Robot-Assisted Hand Movement Training after Stroke
Effectiveness of Robot-Assisted Hand Movement Training after Stroke
批准号:
8248743
负责人:
David Jay Reinkensmeyer
金额:
$28.79万
依托单位国家:
美国
项目类别:
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-03-12 至 2015-02-28
关键词:
AlgorithmsAnatomyBehavioralBiomechanicsBrain imagingCharacteristicsClinicClinicalClinical assessmentsComputer softwareCorticospinal TractsCutaneous MuscleDevicesDiffusion Magnetic Resonance ImagingEffectivenessEsthesiaExerciseFingersFunctional ImagingGoalsHandHome environmentIschemic StrokeJointsLearningLibrariesMeasuresModelingMonitorMotionMotorMotor ActivityMotor outputMovementMuscleOutcomeOutcome MeasureOutputParticipantPatientsPersonsPlayProcessRecording of previous eventsRecoveryRecruitment ActivityRehabilitation therapyRelative (related person)Residual stateResourcesRobotRoboticsRoleSensorySensory ProcessSeriesSkinStrokeSupratentorialTechnologyTestingTrainingTraining ProgramsTraining TechnicsVideo GamesWorkbasebrain tractcostdesignexperiencegraspinsightkinematicspost strokerehabilitation servicerelating to nervous systemresearch studyresponserobot assistancerobotic devicesensorvolunteer
中文摘要
描述(由申请人提供):本项目广泛、长期、科学的目标是确定决定中风后手部运动训练效果的行为和神经解剖学因素。实现这一目标的一个重要的社会影响将是设计出更有效的机器人康复运动技术,这将使中风患者能够提高他们的运动恢复能力,而不是用目前的方法。目前的假设是,中风后的机器人辅助运动训练如果能促进努力的运动输出,从而在关节、肌肉和皮肤上产生相关的、适当的感觉,那么它是最有效的。此外,我们假设这种机器人训练的手部运动恢复量可能受到以下因素的限制:1)从大脑到手部的主要流出道(即皮质脊髓束,CST)被保留了多少;2)感觉处理的完整性;3)以往运动练习的累积历史。这些假设将在一系列的实验中得到验证,这些实验将使用一个机器人设备,当用户每天在家工作时,通过一个有趣的视频游戏库来帮助抓取物体。目的1是确定相关感觉运动活动在机器人辅助运动训练中的益处。在训练过程中,接受机器人的帮助是否有利于增强感觉?机器人设备将帮助参与者练习抓取动作,他们可以开始,但通常无法完成,从而加强关节,肌肉和皮肤的感觉。这种训练技术对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好处。在Aim 1中,用机器人设备辅助运动可能会导致人们松懈,减少他们的力量输出。通过使用先进的机器人控制软件,这个目标将测试增加相对力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训练有效性的影响。在目标1和目标2中测试的机器人辅助训练的版本假定特定的神经资源以获得最佳效果。是否有可能预测谁将从不同形式的机器人辅助手部锻炼中获益最多?在目标1和目标2中,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进行测量,并在3-6个月后训练开始时测量感觉功能和CST损伤程度。假设CST的可用性、感觉功能和以前的运动历史将预测对不同形式的机器人训练的反应。
英文摘要
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.
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EFFECTIVENESS OF ROBOT-ASSISTED HAND MOVEMENT TRAINING AFTER STROKE
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批准号:10643069
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项目类别:
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资助金额:$13.4万
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财政年份:2010
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负责人:David Jay Reinkensmeyer
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依托单位:
Effectiveness of Robot-Assisted Hand Movement Training after Stroke
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资助金额:$0.38万
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财政年份:2009
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负责人:David Jay Reinkensmeyer
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ROBOTICS FOR REHABILITATION THERAPY
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批准号:8166908
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资助金额:$4.42万
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财政年份:2009
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资助金额:$9.06万
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财政年份:2008
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MECHATRONIC TECHNOLOGY FOR STEERING ASSISTANCE
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负责人:David Jay Reinkensmeyer
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