4.5 Article

Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke

Journal

FRONTIERS IN HUMAN NEUROSCIENCE
Volume 15, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fnhum.2021.645021

Keywords

motor recovery; motor compensation; stroke; joint synergy; upper limb; neurorehabilitation; movement analysis

Funding

  1. National Institute of Neurological Disorders and Stroke of the National Institutes of Health [R56 NS100528]
  2. French ministry of health [08-13]

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In the sub-acute phase of stroke affecting the upper extremity, improvements in arm movements are typically seen. A study using kinematic analysis during reaching movements found that recoverers tend to relearn how to generate smooth movements similar to those of healthy individuals, while compensators tend to develop new compensatory movement patterns dissimilar to healthy individuals. These findings can inform neurorehabilitation clinicians on how to provide movement feedback during practice and refine exoskeleton robot therapy to reduce compensatory patterns.
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 similar to 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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