Robot-Assisted Adaptive Training: Custom Force Fields for Teaching Movement Patterns

James L. Patton*, Ferdinando A. Mussa-Ivaldi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

196 Scopus citations

Abstract

Based on recent studies of neuro-adaptive control, we tested a new iterative algorithm to generate custom training forces to "trick" subjects into altering their target-directed reaching movements to a prechosen movement as an after-effect of adaptation. The prechosen movement goal, a sinusoidal-shaped path from start to end point, was never explicitly conveyed to the subject. We hypothesized that the adaptation would cause an alteration in the feedforward command that would result in the prechosen movement. Our results showed that when forces were suddenly removed after a training period of 330 movements, trajectories were significantly shifted toward the prechosen movement. However, de-adaptation occurred (i.e., the after-effect "washed out") in the 50-75 movements that followed the removal of the training forces. A second experiment suppressed vision of hand location and found a detectable reduction in the washout of after-effects, suggesting that visual feedback of error critically influences learning. A final experiment demonstrated that after-effects were also present in the neighborhood of training-44% of original directional shift was seen in adjacent, unpracticed movement directions to targets that were 66° different from the targets used for training. These results demonstrate the potential for these methods for teaching motor skills and for neuro-rehabilitation of brain-injured patients. This is a form of "implicit learning," because unlike explicit training methods, subjects learn movements with minimal instructions, no knowledge of, and little attention to the trajectory.

Original languageEnglish (US)
Pages (from-to)636-646
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Volume51
Issue number4
DOIs
StatePublished - Apr 2004

Keywords

  • Adaptation
  • Control
  • Force fields
  • Haptics
  • Human
  • Human-machine interface
  • Motor learning
  • Robotic neurorehabilitation
  • Robots
  • Teaching

ASJC Scopus subject areas

  • Biomedical Engineering

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