Stephen Bested – Abstract

P24      Mixing robotic guidance and unassisted practice for the learning of a sequential movement

Bested SR & Tremblay L

Perceptual-Motor Behaviour Laboratory, Centre of Motor Control, Faculty of Kinesiology and Physical Education, University of Toronto, Toronto, Canada.

Although robotic guidance has yielded limited effectiveness in improving motor functions in neurologically intact and patient populations1,2, it has typically employed constant practice. Employing variability of practice principles3, our lab has previously employed robotic guidance to acutely improve movement smoothness of a discrete trajectory4. The purpose of the current study was to investigate the impact of physical guidance involving variability of practice on the learning of a sequential movement, namely a golf putt.

The current study employed a pre-test, a training phase, followed by an immediate and a 24-hr post-test. During the pre-test, the kinematic data from the putter’s head was collected and converted into robotic coordinates to be executed using a robot arm, which is highly accurate, consistent, and smooth (see Manson et al., 2014). During training, three groups of novice participants performed putts towards 3 targets (i.e., 192, 213, & 234 cm amplitudes), benefiting from robot guidance on 0%, 50% or 100% of training trials. Only the group that trained with the robot 50% of the trials significantly reduced the endpoint distance and variability between the pre-test and the immediate and/or 24-hr post-test.

This study demonstrates that—following a single acquisition session—the combination of unassisted and robot assisted practice represents the most optimal approach to facilitating short-term learning of a sequential movement. Such work could be relevant to improving putting performance and other sport skills in addition to other practical areas (e.g., rehabilitation).

Acknowledgements: Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), Ontario Research Fund (ORF), University of Toronto

  1. Kümmel, J., Kramer, A., & Gruber, M. (2014). Robotic guidance induces long-lasting changes in the movement pattern of a novel sport-specific motor task. Human Movement Science, 38, 23-33.
  2. Krishnan, C., Ranganathan, R., Kantak, S. S., Dhaher, Y. Y., & Rymer, W. Z. (2012). Active robotic training improves locomotor function in a stroke survivor. Journal of NeuroEngineering and Rehabilitation, 9(1),
  3. Shea, C. H., & Kohl, R. M. (1991). Composition of practice: Influence on the retention of motor skills. Research Quarterly for Exercise and Sport, 62(2), 187-195.
  4. Manson, G. A., Alekhina, M., Srubiski, S. L., Williams, C. K., Bhattacharjee, A., & Tremblay, L. (2014). Effects of robotic guidance on sensorimotor control: Planning vs. online control? Neuro Rehabilitation, 35(4), 689-700.