Motion Evaluation for VR-based Motion Training

Virtual Reality (VR) has emerged as one of the important and effective tools for education and training. Most VRbased training systems are situation-based, where the trainees are trained for discrete decision-making in special situations presented by the VR environments. In contrast, this paper discusses the application of VR to a different class of training, for learning exact motions, often required in sports and the arts. For correct evaluation of the trainee and effective motion training, the reference motion data need to be retargeted according to the body size of the trainee, and performance feedback should be provided to enhance the training effect. This paper focuses on the techniques for retargeting a given reference motion for an arbitrary trainee and providing analysis of how well the trainee has followed the reference motion. We assume that the overall posture of a reference motion is deemed to be more important than the absolute positional profile, and this makes our retargeting formulation different from the previous approaches. On-line quantitative analysis based on a moment by moment comparison between the reference and captured motion data is used to supply indication as to how well the trainee is doing during training. In addition, curve fitting and wavelet transformation are employed for off-line qualitative analysis to generate a corrective advice afterwards. An application of the proposed techniques to a simple swordsman training is demonstrated.