A robot-based interception task to quantify upper limb impairments in proprioceptive and visual feedback after stroke

Kayne Park; Benjamin R Ritsma; Sean P Dukelow; Stephen H Scott

doi:10.1186/s12984-023-01262-0

A robot-based interception task to quantify upper limb impairments in proprioceptive and visual feedback after stroke

J Neuroeng Rehabil. 2023 Oct 11;20(1):137. doi: 10.1186/s12984-023-01262-0.

Authors

Kayne Park¹, Benjamin R Ritsma^{2

3}, Sean P Dukelow⁴, Stephen H Scott^{5

6

7

3}

Affiliations

¹ Centre for Neuroscience Studies, Queen's University, Botterell Hall, 18 Stuart St, Kingston, ON, K7L 3N6, Canada. a.kayne.park@queensu.ca.
² Department of Physical Medicine and Rehabilitation, Queen's University, Kingston, ON, Canada.
³ Providence Care Hospital, Queen's University, Kingston, ON, Canada.
⁴ Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
⁵ Centre for Neuroscience Studies, Queen's University, Botterell Hall, 18 Stuart St, Kingston, ON, K7L 3N6, Canada.
⁶ Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada.
⁷ Department of Medicine, Queen's University, Kingston, ON, Canada.

Abstract

Background: A key motor skill is the ability to rapidly interact with our dynamic environment. Humans can generate goal-directed motor actions in response to sensory stimulus within ~ 60-200ms. This ability can be impaired after stroke, but most clinical tools lack any measures of rapid feedback processing. Reaching tasks have been used as a framework to quantify impairments in generating motor corrections for individuals with stroke. However, reaching may be inadequate as an assessment tool as repeated reaching can be fatiguing for individuals with stroke. Further, reaching requires many trials to be completed including trials with and without disturbances, and thus, exacerbate fatigue. Here, we describe a novel robotic task to quantify rapid feedback processing in healthy controls and compare this performance with individuals with stroke to (more) efficiently identify impairments in rapid feedback processing.

Methods: We assessed a cohort of healthy controls (n = 135) and individuals with stroke (n = 40; Mean 41 days from stroke) in the Fast Feedback Interception Task (FFIT) using the Kinarm Exoskeleton robot. Participants were instructed to intercept a circular white target moving towards them with their hand represented as a virtual paddle. On some trials, the arm could be physically perturbed, the target or paddle could abruptly change location, or the target could change colour requiring the individual to now avoid the target.

Results: Most participants with stroke were impaired in reaction time (85%) and end-point accuracy (83%) in at least one of the task conditions, most commonly with target or paddle shifts. Of note, this impairment was also evident in most individuals with stroke when performing the task using their unaffected arm (75%). Comparison with upper limb clinical measures identified moderate correlations with the FFIT.

Conclusion: The FFIT was able to identify a high proportion of individuals with stroke as impaired in rapid feedback processing using either the affected or unaffected arms. The task allows many different types of feedback responses to be efficiently assessed in a short amount of time.

Keywords: Proprioceptive feedback; Reaction time; Robotic exoskeleton; Stroke; Upper extremity; Visual feedback.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Feedback, Sensory
Humans
Robotics*
Stroke Rehabilitation*
Stroke* / complications
Upper Extremity