Designing Adaptive Tools for Motor Skill Training

We demonstrate the design of adaptive tools for motor skill training that use shape change to automatically vary task difficulty based on a learner’s performance. Studies [1] have shown that automatically-adaptive tools lead to significantly higher learning gains when compared to non-adaptive and manually-adaptive tools. We demonstrate the use of Adapt2Learn [2] - a toolkit that supports designers in building adaptive training tools. Adapt2Learn auto-generates an algorithm that converts a learner’s performance data into adaptation states during motor skill training. This algorithm, that maintains the training difficulty at the ’optimal challenge point’, can be uploaded to the micro-controller to convert several shape-changing tools into adaptive tools for motor skill training. We demonstrate 7 prototypes of adaptive tools for motor-skill learning to show applications in sports, music, rehabilitation, and accessibility.