Altering Movement Patterns in Healthy and Brain-Injured Subjects Via Custom Designed Robotic Forces

Abstract

We investigated robotic methods for teaching movements to hemiparetic subjects using novel techniques for neuro-adaptive control. Eight healthy subjects and twelve hemiparetic stroke subjects were exposed to novel viscous forces during planar movement of the hand towards a visual target. These forces were initially responsible for significant movement errors, but were followed by automatic adaptation. The forces were designed so that unexpected withdrawal would result in a pronounced after-effect, consisting of movement path errors that were opposite in sign to those induced by initial application of the force filed. For healthy subjects, the desired movement was a curved sinusoid. For the hemiparetics, we chose a replicated normal trajectory. After-effect trajectories in healthy subjects' were significantly shifted toward the desired trajectory. This after-effect fully washed out following the removal of the forces in the final 50-75 movements, regardless of whether the subjects had visual feedback of their position. After-effects also generalized to movement directions that were not practiced. Hemiparetics showed different types of results. While several of them showed minimal improvement, the remaining hemiparetics showed adaptation with beneficial after-effects. Furthermore, several in this group retained diminished features of these after- effects for the duration of the experiment. This approach may be an effective neurorehabilitation tool because it does not require explicit instructions about the desired movement.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA409640

Entities

People

  • F. A. Mussa-ivaldi
  • J. L. Patton
  • W. Z. Rymer

Organizations

  • Northwestern University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Classification
  • Dynamics
  • Engineering
  • Engineers
  • Guidance
  • Learning
  • Machine Learning
  • Military Research
  • Nervous System
  • Rehabilitation
  • Robots
  • Schools
  • Standards
  • Training
  • Trajectories

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy
  • Autonomy - Autonomous System Control