Skill Learning-support by segmentation of posture change

Abstract

Deep investigation and exploration about how to move body in performing a skill is crucially important for athletes. Meta-cognitive verbalization is one method for doing that. Recent studies on meta-cognition (e.g. [2]), although having shown its effectiveness in acquiring embodied skills, have pointed out the necessity of supportive environments and methodologies for athletes to continue meta-cognitive activities and get inspired for new discovery. Visualization of body movements and its quick feedback to athletes seem to be significant for that support. This study presents a supportive software environment in which athletes are able to easily interpret how his or her body posture changed during one trial of performance, e.g. batting swing in baseball, and compare multiple trials. Rough segmentation of body posture along the time frame is the key idea, enabling easy interpretation of one's own posture by athletes and promoting meta-cognition. Simply representing body by five triangles and representing body posture by relationships of those triangles are the basis for that rough segmentation.