Particular motions are important to play sports with high performance. The particular motions are mastered by learning motions, and visual information is considered to be effective for understanding and learning motions. In recent years, HMD with VR has been introduced as a new tool for learning motions with visual information. An advantage of the HMD-based motion learning method is that it enables learners to switch their observation view. Here, this research investigates basic view characteristics of observing and reproducing particular dynamic motions, which would be necessary to develop some methods for switching observation view properly. An experiment was conducted in order to study the basic view characteristics. As for the observation view factor, we prepared two factor levels, one was the front mirror view, and the other the rear camera view. In the experiment, a subject recognized and reproduced some reference dynamic motions on real time with each of the two views. The experimental results revealed that the reproduction performance with the rear camera view was significantly better than that with the front mirror view in the case of the depth-directional motions, compared with the other case of the depth-uncorrelated motions. It should be noted that the difference in the motion reproduction may become crucial for learners in particular as the motion velocity increases. It is supposed that the observation with the front mirror view requires some mental transformation operation when the learners reproduce motions. In selecting the observation view, it is required to minimize the mental transformation operation. The requirement is expected to be satisfied with the rear camera view, provided that occlusions are not crucial for learners to observe reference motions.
Shin Kinoshita, Yoshihiko Nomura, Ryota Sakamoto, Tokuhiro Sugiura, "Recognition and reproduction performance of hand motions with HMD-based motion learning method" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision, 2018, pp 125-1 - 125-5, https://doi.org/10.2352/ISSN.2470-1173.2018.09.IRIACV-125