Investigation of anticipation in motor control using kinematic and kinetic metrics in a leader-follower task

Date

2025-4-10

Author

Eşme, İrem

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Understanding the processes of motor learning and skill acquisition is crucial in domains ranging from rehabilitation to performance training. This thesis investigates the role of anticipation mechanisms in a controlled leader-follower task, aiming to enhance motor adaptation through structured training protocols. Participants engaged in a virtual reality-based task using a gamepad to follow a dynamic leader, whose visibility was manipulated via deterministic and stochastic paradigms to elicit anticipatory behavior. Real-time feedback and data collection enabled analysis of motor learning trajectories. A comprehensive set of novel kinematic and kinetic metrics—adapted from robotics and time series analysis—was employed to assess movement synchrony, smoothness, and coordination. These metrics captured not only endpoint performance but also learning dynamics across phases. Significant improvements were observed in all groups from baseline to retention. However, group-level comparisons revealed no clear differences in final performance across protocols, including between stochastic and deterministic anticipation groups. Critically, slope-based analyses showed that anticipation-based training protocols were associated with faster learning rates in the full sample and among beginners. These effects were most pronounced in metrics such as score pause duration, temporal lag, and spatial error. The findings suggest that although final outcomes converge across groups, the rate and trajectory of motor learning are shaped by the training protocol. The developed platform enabled high-resolution behavioral analysis and showed potential as a tool for adaptive motor training and assessment. Future studies should examine individual differences in perception and adaptation and expand sample sizes to distinguish the impacts of different anticipation strategies.

Subject Keywords

Motor Learning, Motor Performance Evaluation, Anticipation Mechanism, Robotic Assisted Training, Leader Follower Dynamics, Motor Control

URI

https://hdl.handle.net/11511/114487

Collections

Graduate School of Natural and Applied Sciences, Thesis