Enhancing Motor Learning in Cycling Tasks
The Role of Model Predictive Control and Training Sequence
L. Alizadehsaravi (TU Delft - Biomechatronics & Human-Machine Control)
S. Drauksas (Student TU Delft)
J.K. Moore (TU Delft - Biomechatronics & Human-Machine Control)
R. Happee (TU Delft - Intelligent Vehicles)
L. M. Marchal (Erasmus MC, TU Delft - Human-Robot Interaction)
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Abstract
We evaluated the impact of Model Predictive Control (MPC) robotic-assisted versus unassisted training on motor learning of a complex bicycle steering task. Ten participants were divided into two groups, alternating between MPC-assisted and unassisted training to ride a steer-by-wire bicycle on a treadmill to collect virtual stars. At Baseline, Mid-Training, and Post-Training, motor skills were assessed by the average and standard deviation (SD) of distance to stars, while performance was measured by the mean absolute and SD of the steering rate. We found significant improvements in task skill and steering performance, with notable benefits observed in the performance of the group initially trained unassisted. Our findings suggest that starting the training unassisted could stimulate an internal focus (concentrating on one's own body movements) and intrinsic skill perception. This foundation may then form a basis for later integration of MPC assistance to refine further the gained motor skills. Such a sequential training approach may benefit motor skill acquisition of complex dynamics tasks. Further research is necessary to validate and apply these findings to enhance training methods.