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Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke

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 po...

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Autores principales: Nibras, Nadir, Liu, Chang, Mottet, Denis, Wang, Chunji, Reinkensmeyer, David, Remy-Neris, Olivier, Laffont, Isabelle, Schweighofer, Nicolas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120113/
https://www.ncbi.nlm.nih.gov/pubmed/33994981
http://dx.doi.org/10.3389/fnhum.2021.645021
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author Nibras, Nadir
Liu, Chang
Mottet, Denis
Wang, Chunji
Reinkensmeyer, David
Remy-Neris, Olivier
Laffont, Isabelle
Schweighofer, Nicolas
author_facet Nibras, Nadir
Liu, Chang
Mottet, Denis
Wang, Chunji
Reinkensmeyer, David
Remy-Neris, Olivier
Laffont, Isabelle
Schweighofer, Nicolas
author_sort Nibras, Nadir
collection PubMed
description 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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spelling pubmed-81201132021-05-15 Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke Nibras, Nadir Liu, Chang Mottet, Denis Wang, Chunji Reinkensmeyer, David Remy-Neris, Olivier Laffont, Isabelle Schweighofer, Nicolas Front Hum Neurosci Neuroscience 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. Frontiers Media S.A. 2021-04-30 /pmc/articles/PMC8120113/ /pubmed/33994981 http://dx.doi.org/10.3389/fnhum.2021.645021 Text en Copyright © 2021 Nibras, Liu, Mottet, Wang, Reinkensmeyer, Remy-Neris, Laffont and Schweighofer. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Nibras, Nadir
Liu, Chang
Mottet, Denis
Wang, Chunji
Reinkensmeyer, David
Remy-Neris, Olivier
Laffont, Isabelle
Schweighofer, Nicolas
Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke
title Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke
title_full Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke
title_fullStr Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke
title_full_unstemmed Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke
title_short Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke
title_sort dissociating sensorimotor recovery and compensation during exoskeleton training following stroke
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120113/
https://www.ncbi.nlm.nih.gov/pubmed/33994981
http://dx.doi.org/10.3389/fnhum.2021.645021
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