Abstract

Learning to control forces is known to reduce the amount of movement variability (e.g.; standard deviation; SD) while also altering the temporal structure of movement variability (e.g.; approximate entropy; ApEn). Such variability control has not been explored in stroke survivors during reaching movements in dynamic environments. Whether augmented feedback affects such variability control, is also unknown. Chronic stroke survivors, assigned randomly to a control/experimental group, learned reaching movements in a dynamically changing environment while receiving either true feedback of their movement (control) or augmented visual feedback (experimental). Hand movement variability was analyzed using SD and ApEn. A significant change in variability was determined for both SD and ApEn. Post hoc tests revealed that the significant decrease in SD was not retained after a week. However, the significant increase in ApEn, determined on both days of training, showed significant retention effects. In dynamically changing environments, chronic stroke survivors reduced the amount of movement variability and made their movement patterns less repeatable and possibly more flexible. These changes were not affected by augmented visual feedback. Moreover, the learning patterns characteristically involved the control of the nonlinear dynamics rather than the amount of hand movement variability. The absence of transfer effects demonstrated that variability control of hand movement after a stroke is specific to the task and the environment.

Original languageEnglish (US)
Pages (from-to)366-376
Number of pages11
JournalAnnals of biomedical engineering
Volume41
Issue number2
DOIs
StatePublished - Jan 1 2013

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Feedback
Force control
Entropy

Keywords

  • Approximate entropy
  • Augmented feedback
  • Force fields
  • Nonlinear dynamics
  • Robotics
  • Upper extremity

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

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title = "Stroke survivors control the temporal structure of variability during reaching in dynamic environments",
abstract = "Learning to control forces is known to reduce the amount of movement variability (e.g.; standard deviation; SD) while also altering the temporal structure of movement variability (e.g.; approximate entropy; ApEn). Such variability control has not been explored in stroke survivors during reaching movements in dynamic environments. Whether augmented feedback affects such variability control, is also unknown. Chronic stroke survivors, assigned randomly to a control/experimental group, learned reaching movements in a dynamically changing environment while receiving either true feedback of their movement (control) or augmented visual feedback (experimental). Hand movement variability was analyzed using SD and ApEn. A significant change in variability was determined for both SD and ApEn. Post hoc tests revealed that the significant decrease in SD was not retained after a week. However, the significant increase in ApEn, determined on both days of training, showed significant retention effects. In dynamically changing environments, chronic stroke survivors reduced the amount of movement variability and made their movement patterns less repeatable and possibly more flexible. These changes were not affected by augmented visual feedback. Moreover, the learning patterns characteristically involved the control of the nonlinear dynamics rather than the amount of hand movement variability. The absence of transfer effects demonstrated that variability control of hand movement after a stroke is specific to the task and the environment.",
keywords = "Approximate entropy, Augmented feedback, Force fields, Nonlinear dynamics, Robotics, Upper extremity",
author = "Mukul Mukherjee and Panagiotis Koutakis and Siu, {Joseph Ka-Chun} and Fayad, {Pierre B} and Nicholas Stergiou",
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AB - Learning to control forces is known to reduce the amount of movement variability (e.g.; standard deviation; SD) while also altering the temporal structure of movement variability (e.g.; approximate entropy; ApEn). Such variability control has not been explored in stroke survivors during reaching movements in dynamic environments. Whether augmented feedback affects such variability control, is also unknown. Chronic stroke survivors, assigned randomly to a control/experimental group, learned reaching movements in a dynamically changing environment while receiving either true feedback of their movement (control) or augmented visual feedback (experimental). Hand movement variability was analyzed using SD and ApEn. A significant change in variability was determined for both SD and ApEn. Post hoc tests revealed that the significant decrease in SD was not retained after a week. However, the significant increase in ApEn, determined on both days of training, showed significant retention effects. In dynamically changing environments, chronic stroke survivors reduced the amount of movement variability and made their movement patterns less repeatable and possibly more flexible. These changes were not affected by augmented visual feedback. Moreover, the learning patterns characteristically involved the control of the nonlinear dynamics rather than the amount of hand movement variability. The absence of transfer effects demonstrated that variability control of hand movement after a stroke is specific to the task and the environment.

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