ImmerTai : immersive motion learning in VR environments

• Main Authors: Chen, Xiaoming, Chen, Zhibo, Li, Ye, He, Tianyu, Hou, Junhui, Liu, Sen, He, Ying
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Immersive Education; Motion Training
• Online Access: https://hdl.handle.net/10356/144747
• Institution: Nanyang Technological University

Immersive learning in Virtual Reality (VR) environments is the developing trend for future education systems including remote physical training. This paper presents “ImmerTai”, a system that is designed for effective remote motion training, particularly for Chinese Taichi, in an immersive way. With ImmerTai, the Taichi expert’s motion is captured and delivered to remote students in CAVE, HMD and PC environments for learning. The students’ motions are also captured for motion quality assessment and a group of students can form a virtual collaborative learning scenario. We built up a Taichi motion dataset with ground truth of motion quality, and based on this, we developed and evaluated several motion quality assessment methods. Then, user tests were designed and carried out to measure and compare the learning outcomes (learning time, quality and overall efficiency) of students in Cave Automatic Virtual Environment (CAVE), Head Mounted Display (HMD) and Personal Computer (PC) environments. Meanwhile, the connections between students’ learning outcomes and their VR experience were investigated and discussed too. Our results show that ImmerTai can accelerate the learning process of students noticeably (up to 17%) compared to non-immersive learning with the conventional PC setup. However, we observed a substantial difference in the quality of the learnt motion between CAVE (26% gain) and HMD (23% drop) compared to PC (baseline). While strong VR presence can enhance the learning experience of students, their learning outcomes are not fully consistent to their experience. Overall, ImmerTai with CAVE demonstrated a significantly higher learning efficiency than other tested environments.