Effect of robotic performance-based error-augmentation versus error-reduction training on the gait of healthy individuals

Gait Posture. 2013 Jan;37(1):113-20. doi: 10.1016/j.gaitpost.2012.06.025. Epub 2012 Jul 24.

Abstract

Effective locomotion training with robotic exoskeletons requires identification of optimal control algorithms to better facilitate motor learning. Two commonly employed training protocols emphasize use of training stimuli that either augment or reduce performance errors. The current study sought to identify which of these training strategies promote better short-term modification of a typical gait pattern in healthy individuals as a framework for future application to neurologically impaired individuals. Ten subjects were assigned to each of a performance-based error-augmentation or error-reduction training group. All subjects completed a 45-min session of treadmill walking at their preferred speed with a robotic exoskeleton. Target templates prescribed an ankle path for training that corresponded to an increased step height. When subjects' instantaneous ankle positions fell below the inferior virtual wall of the target ankle path, robotic forces were applied that either decreased (error-reduction) or increased (error-augmentation) the deviation from the target path. When the force field was turned on, both groups walked with ankle paths better approximating the target template compared to baseline. When the force field was removed unexpectedly during catch and post-training trials, only the error-augmentation group maintained an ankle path close to the target ankle path. Further investigation is required to determine if a similar training advantage is provided for neurologically impaired individuals.

Publication types

  • Randomized Controlled Trial
  • Research Support, N.I.H., Extramural

MeSH terms

  • Adult
  • Biomechanical Phenomena
  • Cues
  • Feedback, Sensory*
  • Female
  • Gait Disorders, Neurologic / rehabilitation*
  • Gait*
  • Humans
  • Locomotion
  • Male
  • Orthotic Devices*
  • Robotics*
  • Time Factors