Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke.
Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke.
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DOI：
10.3389/fnhum.2021.645021
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发表时间：
2021
影响因子：
2.9
通讯作者：
Schweighofer N
中科院分区：
文献类型：
--
作者：
Nibras N;Liu C;Mottet D;Wang C;Reinkensmeyer D;Remy-Neris O;Laffont I;Schweighofer N
在中风的亚急性期，手臂运动的质量通常会改善，影响上肢。在这里，我们使用整个手臂的运动学分析来区分这些改善是由于真正的恢复还是由于补偿。53名急性中风后的参与者在使用ArmeoSpring外骨骼进行为期4周的训练期间进行了∼80到达运动测试。所有参与者都表现出末端执行器性能的改善，这是通过运动平稳性来衡量的。记录并分析肩部水平旋转(SH)、肩部抬高(SE)、肘部旋转和前臂旋转4个ArmeoSpring角度。我们首先通过对年轻对照受试者在触达测试中记录的这四个关节速度进行稀疏主成分分析来表征健康的关节协调模式。我们发现，两个主要的关节相关性[SH与肘关节旋转和SE与前臂旋转]解释了关节速度数据95%以上的方差。我们通过比较所有测试中这两种相关性的演变，确定了两组中风参与者。在“恢复者”组(N=19)中，两个联合相关项对于控制组参与者而言都收敛到各自的相关项。因此，恢复者重新学习了如何产生平稳的末端执行器运动，同时发展出类似于对照组参与者的关节运动模式。在“补偿者”组(N=34)中，两个联合相关性中至少有一个偏离了对照参与者的相应相关性。补偿者通过发现不同于对照组参与者的各种新的补偿性运动模式，重新学习了如何产生平稳的末端效应器运动。新的代偿模式包括SE关节和前臂关节的非典型脱钩，SH旋转关节和肘关节的非典型耦合。无论是在训练开始时还是训练结束时，上肢Fugl-Meyer量表评估的临床损害水平在两组之间都没有差异。然而，在训练开始时，恢复者在末端执行器运动平稳性方面的改善明显快于补偿者。我们的分析可以用来告知神经康复临床医生如何在练习中提供运动反馈，并建议改进外骨骼机器人治疗以减少代偿模式的途径。
The quality of arm movements typically improves in the sub-acute phase of stroke affecting the upper extremity. Here, we used whole arm kinematic analysis during reaching movements to distinguish whether these improvements are due to true recovery or to compensation. Fifty-three participants with post-acute stroke performed ∼80 reaching movement tests during 4 weeks of training with the ArmeoSpring exoskeleton. All participants showed improvements in end-effector performance, as measured by movement smoothness. Four ArmeoSpring angles, shoulder horizontal (SH) rotation, shoulder elevation (SE), elbow rotation, and forearm rotation, were recorded and analyzed. We first characterized healthy joint coordination patterns by performing a sparse principal component analysis on these four joint velocities recorded during reaching tests performed by young control participants. We found that two dominant joint correlations [SH with elbow rotation and SE with forearm rotation] explained over 95% of variance of joint velocity data. We identified two clusters of stroke participants by comparing the evolution of these two correlations in all tests. In the “Recoverer” cluster (N = 19), both joint correlations converged toward the respective correlations for control participants. Thus, Recoverers relearned how to generate smooth end-effector movements while developing joint movement patterns similar to those of control participants. In the “Compensator” cluster (N = 34), at least one of the two joint correlations diverged from the corresponding correlation of control participants. Compensators relearned how to generate smooth end-effector movements by discovering various new compensatory movement patterns dissimilar to those of control participants. New compensatory patterns included atypical decoupling of the SE and forearm joints, and atypical coupling of the SH rotation and elbow joints. There was no difference in clinical impairment level between the two groups either at the onset or at the end of training as assessed with the Upper Extremity Fugl-Meyer scale. However, at the start of training, the Recoverers showed significantly faster improvements in end-effector movement smoothness than the Compensators. Our analysis can be used to inform neurorehabilitation clinicians on how to provide movement feedback during practice and suggest avenues for refining exoskeleton robot therapy to reduce compensatory patterns.
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影响因子：
2.5
作者：
McCrea, PH;Eng, JJ;Hodgson, AJ
通讯作者：
Hodgson, AJ
影响因子：
4.2
作者：
Park H;Kim S;Winstein CJ;Gordon J;Schweighofer N
通讯作者：
Schweighofer N
影响因子：
2.5
作者：
Bakhti, K. K. A.;Mottet, D.;Laffont, I.
通讯作者：
Laffont, I.
影响因子：
4.2
作者：
Cortes, Juan C.;Goldsmith, Jeff;Kitago, Tomoko
通讯作者：
Kitago, Tomoko
DOI：
10.1109/tnsre.2011.2157705
发表时间：
2011-08-01
影响因子：
4.9
作者：
Brokaw, Elizabeth B.;Black, Iian;Lum, Peter S.
通讯作者：
Lum, Peter S.