首页> 美国卫生研究院文献>Frontiers in Neuroscience >Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation
【2h】

Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation

机译:虚拟现实伸手掌握训练与外骨骼辅助中风康复过程中的闭环任务难度适应

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

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.
机译:患有严重上肢运动障碍的中风患者可在多关节外骨骼的辅助下进行康复锻炼。尽管此技术可以进行密集的面向任务的培训,但当援助过于支持时,也可能导致工作量减少。因此,在锻炼过程中保持患者的参与并提供“所需的帮助”仍然是一个持续的挑战。我们应用了市售的七个自由度手臂外骨骼,以在虚拟环境中的面向任务的训练过程中提供被动重力补偿。在为期4周的初步研究中,五名受严重影响的慢性中风患者进行了类似于日常生活的触手可及的锻炼。受试者从其三维运动中收到虚拟现实反馈。锻炼的难易程度通过性能相关的实时自适应算法进行调整。该算法的目标是自动改善运动范围。在20次培训和反馈过程中,这种无监督的自适应培训概念导致虚拟培训空间根据受试者的能力逐渐增加(p <0.001)。该学习曲线与实际运动学参数(即运动范围(p = 0.008),运动精度(p = 0.01)和运动速度(p <0.001))的同时改进并列。值得注意的是,这些运动学上的提高与运动能力的改善并驾齐驱,例如增加的肘部运动(p = 0.001),抓地力(p <0.001)和上肢Fugl-Meyer评估得分从14.3±5增至16.9±6.1(p = 0.026) )。在虚拟现实中将重力补偿辅助与自适应闭环反馈相结合,可为重症中风患者提供定制的康复环境。这种方法可以根据个人的功能恢复能力逐步挑战受试者,从而促进运动学习。可能有必要同时应用恢复性干预措施,以将这些改善转化为严重运动障碍患者在日常生活中的相关功能获得。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号