Learning to perform a novel movement pattern using haptic guidance: slow learning, rapid forgetting, and attractor paths

Mechanical guidance is a common technique to teach patients desired movement patterns during motor rehabilitation, but little is known about the motor learning processes involved with this technique. In this study we examined how well unimpaired subjects could learn to trace a novel path after they practiced it with mechanical guidance from a robot. The form of haptic guidance used was a virtual channel that constrained the hand to follow the desired path (a snake-like curve). Subjects substantially improved their ability to trace the path following practice with haptic guidance, relative to their performance following an initial visual demonstration. They slowly improved their performance with more haptic training. However, when asked to reproduce the path repeatedly, their performance degraded over the course of a few trials. The tracing errors were not random, but instead were consistent with a systematic evolution toward another path, as if being drawn to an "attractor path". These results suggest that haptic demonstration can improve short-term performance of a novel desired trajectory. However, in the short term, the motor system is inclined to repeat its mistakes following just a few movements without guidance.

[1]  Steven C Cramer,et al.  Robotics, motor learning, and neurologic recovery. , 2004, Annual review of biomedical engineering.

[2]  C. Burgar,et al.  Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke. , 2002, Archives of physical medicine and rehabilitation.

[3]  V. Dietz,et al.  Driven gait orthosis for improvement of locomotor training in paraplegic patients , 2001, Spinal Cord.

[4]  Cordula Werner,et al.  Partial body weight supported treadmill training for gait recovery following stroke. , 2003, Advances in neurology.

[5]  J. F. Soechting,et al.  Haptic synthesis of shapes and sequences. , 2004, Journal of neurophysiology.

[6]  D.J. Reinkensmeyer,et al.  Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  N. Hogan,et al.  The effect of robot-assisted therapy and rehabilitative training on motor recovery following stroke. , 1997, Archives of neurology.

[8]  Ferdinando A. Mussa-Ivaldi,et al.  Robot-assisted adaptive training: custom force fields for teaching movement patterns , 2004, IEEE Transactions on Biomedical Engineering.

[9]  W. Rymer,et al.  Comparison of Robot-Assisted Reaching to Free Reaching in Promoting Recovery From Chronic Stroke , 2001 .

[10]  N. Hogan,et al.  Interactive robots for neuro-rehabilitation. , 2004, Restorative neurology and neuroscience.

[11]  Frank Tendick,et al.  Haptic guidance: experimental evaluation of a haptic training method for a perceptual motor skill , 2002, Proceedings 10th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. HAPTICS 2002.

[12]  S. Hesse,et al.  Upper and lower extremity robotic devices for rehabilitation and for studying motor control , 2003, Current opinion in neurology.