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 field. 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 neurohabilitation tool because it does not require explicit instructions about the desired movement.
Original language | English (US) |
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Pages (from-to) | 1356-1359 |
Number of pages | 4 |
Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
Volume | 2 |
State | Published - 2001 |
Event | 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Istanbul, Turkey Duration: Oct 25 2001 → Oct 28 2001 |
Keywords
- Adaptation
- Dynamics
- Learning
- Model
- Motor control
- Robot
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics