Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation.
Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation.
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闭环任务难度适应虚拟现实中的到达训练训练有助于进行中风康复的外骨骼。
DOI：
10.3389/fnins.2016.00518
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发表时间：
2016
影响因子：
4.3
通讯作者：
Gharabaghi A
中科院分区：
文献类型：
--
作者：
Grimm F;Naros G;Gharabaghi A
有严重上肢运动障碍的中风患者可以在多关节外骨骼的帮助下进行康复锻炼。虽然这项技术可以实现高强度的任务导向型培训，但当援助过于支持时，它也可能导致松懈。因此，在演习期间提供“按需协助”的同时，保持患者的参与度仍然是一个持续的挑战。我们应用了一种商用的七自由度手臂外骨骼，在虚拟环境中的任务型训练中提供被动重力补偿。在这项为期4周的初步研究中，5名受严重影响的慢性中风患者进行了类似日常生活活动的伸展抓握练习。受试者从他们的三维运动中得到虚拟现实的反馈。练习的难度通过一种依赖于性能的实时适应算法进行调整。该算法的目标是自动改善运动范围。在20次训练和反馈的过程中，这种无监督的适应性训练理念导致了虚拟训练空间(p<；0.001)根据受试者的能力而逐渐增加。这条学习曲线与真实世界运动学参数的同步改善是平行的，即运动范围(p=0.008)、运动精度(p=0.01)和运动速度(p<；0.001)。值得注意的是，这些运动学方面的改善与运动改善是平行的，例如增加肘关节活动度(p=0.001)，握力(p<；0.001)，以及上肢Fugl-Meyer评估评分从14.3±5增加到16.9±6.1分(p=0.026)。将重力补偿辅助与虚拟现实中的自适应闭环反馈相结合，为严重中风患者提供定制的康复环境。这种方法可以通过根据个体的功能恢复能力逐步挑战受试者来促进运动学习。可能有必要同时应用恢复性干预措施，将这些改善转化为严重运动障碍患者在日常生活活动中的相关功能收益。
Stroke patients with severe motor deficits of the upper extremity may practice rehabilitation exercises with the assistance of a multi-joint exoskeleton. Although this technology enables intensive task-oriented training, it may also lead to slacking when the assistance is too supportive. Preserving the engagement of the patients while providing “assistance-as-needed” during the exercises, therefore remains an ongoing challenge. We applied a commercially available seven degree-of-freedom arm exoskeleton to provide passive gravity compensation during task-oriented training in a virtual environment. During this 4-week pilot study, five severely affected chronic stroke patients performed reach-to-grasp exercises resembling activities of daily living. The subjects received virtual reality feedback from their three-dimensional movements. The level of difficulty for the exercise was adjusted by a performance-dependent real-time adaptation algorithm. The goal of this algorithm was the automated improvement of the range of motion. In the course of 20 training and feedback sessions, this unsupervised adaptive training concept led to a progressive increase of the virtual training space (p < 0.001) in accordance with the subjects' abilities. This learning curve was paralleled by a concurrent improvement of real world kinematic parameters, i.e., range of motion (p = 0.008), accuracy of movement (p = 0.01), and movement velocity (p < 0.001). Notably, these kinematic gains were paralleled by motor improvements such as increased elbow movement (p = 0.001), grip force (p < 0.001), and upper extremity Fugl-Meyer-Assessment score from 14.3 ± 5 to 16.9 ± 6.1 (p = 0.026). Combining gravity-compensating assistance with adaptive closed-loop feedback in virtual reality provides customized rehabilitation environments for severely affected stroke patients. This approach may facilitate motor learning by progressively challenging the subject in accordance with the individual capacity for functional restoration. It might be necessary to apply concurrent restorative interventions to translate these improvements into relevant functional gains of severely motor impaired patients in activities of daily living.
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影响因子：
4.3
作者：
Grimm F;Gharabaghi A
通讯作者：
Gharabaghi A
影响因子：
158.5
作者：
Dobkin, BH
通讯作者：
Dobkin, BH
影响因子：
4.7
作者：
Bauer, Robert;Vukelic, Mathias;Gharabaghi, Alireza
通讯作者：
Gharabaghi, Alireza
影响因子：
2
作者：
Boyd, LA;Winstein, CJ
通讯作者：
Winstein, CJ
DOI：
10.1080/03610927708827533
发表时间：
1977-01-01
期刊：
COMMUNICATIONS IN STATISTICS PART A-THEORY AND METHODS
影响因子：
--
作者：
HOLLAND, PW;WELSCH, RE
通讯作者：
WELSCH, RE