The Influence of Robotic Guidance on Different Types of Motor Timing

ABSTRACT

Robotic guidance as a means to facilitate motor learning and rehabilitation has received considerable attention during the last few years. However, mixed outcomes suggest that the benefits might be restricted to certain movement characteristics. The authors investigate the effects of robotic guidance on different kinds of motor timing. Two groups of participants performed 2 variants of a circle drawing task in a synchronization-continuation paradigm. One variant was continuous circle drawing (emergent timing), the other variant was intermittent circle drawing (event-based timing). The total duration of movement cycles (absolute timing) and the relative duration of submovements (relative timing) were measured. Half of the participants in each group were guided by a robot device during synchronization (robot-guided group), the other half of the participants received no guidance (control group). Guided participants had superior performance during the synchronization phase with both timing tasks. During continuation there were no benefits of haptic guidance anymore with the continuous circle drawing task. In contrast, with the intermittent circle drawing task guided participants were more accurate in their relative timing than control participants already in the first few trials, and this advantage did not increase in the course of practice. The benefit is thus rather immediate and not cumulative. This finding is consistent with the notion that movement characteristics such as relative timing, which are hard to demonstrate visually or verbally, profit from robotic guidance because of the more accurate demonstrations of the correct movements.

Keywords:

• motor timing
• robotic guidance
• emergent timing
• event-based timing
• motor learning

ACKNOWLEDGMENTS

The research reported in this article was supported by the European Community's Seventh Framework Programme, Grant Agreement Number 231724 (HUMOUR). The authors thank Gennadiy Bardachev for software support and Eva Hanisch, Jennifer Stube, Maia Iobidze, and Sarah Jacobs for support in running the experiment.