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In this research, we propose a method to support sports instruction by displaying visualized comparison between a model motion and that of the player. Human pose estimation with the OpenPose framework based on deep learning technologies is first applied to both model and user motions. The motion difference between the model and the user is visualized with their trajectories superimposed on those two motions. To help users improve their motions and forms, our system displays where the specific body part, for example, the elbow of the throwing arm, should be located at a specific time, computed from the location and timing in the model motion. Our system also presents changes of velocity and position of body parts over time with line graphs for supposing users better understand that motion. Our current implementation is designed for pitching motions, and several other motions is included in our future work. We conducted user experiments where four participants used our system to improve their pitching forms. After recording pitching motion of these four participants, they were divided into two groups. First two participants were given the visualizations of our system while other two were presented only a pair of motions from a model and their own. The participants then performed their improved pitching motions again in front of our system to evaluate which of these two groups showed more refinements. The experimental results have shown that their forms are improved in both groups, and the usability of the proposed system has not successfully tested. Future work includes conducting larger-scale experiments, automatic selection of model motions for each user, as well as extending our method to other motions in different sports.
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