Immersive and collaborative Taichi motion learning in various VR environments

Tianyu He; Xiaoming Chen; Zhibo Chen; Ye Li; Sen Liu; Junhui Hou; Ying He

doi:10.1109/VR.2017.7892299

Immersive and collaborative Taichi motion learning in various VR environments

Tianyu He
, Xiaoming Chen^*
, Zhibo Chen^*
, Ye Li
, Sen Liu
, Junhui Hou
, Ying He

^*Corresponding author for this work

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

20 Citations (Scopus)

Abstract

Learning 'motion' online or from video tutorials is usually inefficient since it is difficult to deliver 'motion' information in traditional ways and in the ordinary PC platform. This paper presents ImmerTai, a system that can efficiently teach motion, in particular Chinese Taichi motion, in various immersive environments. ImmerTai captures the Taichi expert's motion and delivers to students the captured motion in multi-modal forms in immersive CAVE, HMD as well as ordinary PC environments. The students' motions are captured too for quality assessment and utilized to form a virtual collaborative learning atmosphere. We built up a Taichi motion dataset with 150 fundamental Taichi motions captured from 30 students, on which we evaluated the learning effectiveness and user experience of ImmerTai. The results show that ImmerTai can enhance the learning efficiency by up to 17.4% and the learning quality by up to 32.3%.

Original language	English
Title of host publication	Proceedings - IEEE Virtual Reality
Publisher	IEEE Computer Society
Pages	307-308
ISBN (Print)	9781509066476
DOIs	https://doi.org/10.1109/VR.2017.7892299
Publication status	Published - 4 Apr 2017
Event	19th IEEE Virtual Reality, VR 2017 - Los Angeles, United States Duration: 18 Mar 2017 → 22 Mar 2017

Conference

Conference	19th IEEE Virtual Reality, VR 2017
Place	United States
City	Los Angeles
Period	18/03/17 → 22/03/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education

Research Keywords

Immersive education
Motion training
VR

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10.1109/VR.2017.7892299

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@inproceedings{005f79ecc55144df9200ed79d38c229e,

title = "Immersive and collaborative Taichi motion learning in various VR environments",

abstract = "Learning 'motion' online or from video tutorials is usually inefficient since it is difficult to deliver 'motion' information in traditional ways and in the ordinary PC platform. This paper presents ImmerTai, a system that can efficiently teach motion, in particular Chinese Taichi motion, in various immersive environments. ImmerTai captures the Taichi expert's motion and delivers to students the captured motion in multi-modal forms in immersive CAVE, HMD as well as ordinary PC environments. The students' motions are captured too for quality assessment and utilized to form a virtual collaborative learning atmosphere. We built up a Taichi motion dataset with 150 fundamental Taichi motions captured from 30 students, on which we evaluated the learning effectiveness and user experience of ImmerTai. The results show that ImmerTai can enhance the learning efficiency by up to 17.4\% and the learning quality by up to 32.3\%.",

keywords = "Immersive education, Motion training, VR",

author = "Tianyu He and Xiaoming Chen and Zhibo Chen and Ye Li and Sen Liu and Junhui Hou and Ying He",

year = "2017",

month = apr,

day = "4",

doi = "10.1109/VR.2017.7892299",

language = "English",

isbn = "9781509066476",

pages = "307--308",

booktitle = "Proceedings - IEEE Virtual Reality",

publisher = "IEEE Computer Society",

address = "United States",

note = "19th IEEE Virtual Reality, VR 2017 ; Conference date: 18-03-2017 Through 22-03-2017",

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TY - GEN

T1 - Immersive and collaborative Taichi motion learning in various VR environments

AU - He, Tianyu

AU - Chen, Xiaoming

AU - Chen, Zhibo

AU - Li, Ye

AU - Liu, Sen

AU - Hou, Junhui

AU - He, Ying

PY - 2017/4/4

Y1 - 2017/4/4

N2 - Learning 'motion' online or from video tutorials is usually inefficient since it is difficult to deliver 'motion' information in traditional ways and in the ordinary PC platform. This paper presents ImmerTai, a system that can efficiently teach motion, in particular Chinese Taichi motion, in various immersive environments. ImmerTai captures the Taichi expert's motion and delivers to students the captured motion in multi-modal forms in immersive CAVE, HMD as well as ordinary PC environments. The students' motions are captured too for quality assessment and utilized to form a virtual collaborative learning atmosphere. We built up a Taichi motion dataset with 150 fundamental Taichi motions captured from 30 students, on which we evaluated the learning effectiveness and user experience of ImmerTai. The results show that ImmerTai can enhance the learning efficiency by up to 17.4% and the learning quality by up to 32.3%.

AB - Learning 'motion' online or from video tutorials is usually inefficient since it is difficult to deliver 'motion' information in traditional ways and in the ordinary PC platform. This paper presents ImmerTai, a system that can efficiently teach motion, in particular Chinese Taichi motion, in various immersive environments. ImmerTai captures the Taichi expert's motion and delivers to students the captured motion in multi-modal forms in immersive CAVE, HMD as well as ordinary PC environments. The students' motions are captured too for quality assessment and utilized to form a virtual collaborative learning atmosphere. We built up a Taichi motion dataset with 150 fundamental Taichi motions captured from 30 students, on which we evaluated the learning effectiveness and user experience of ImmerTai. The results show that ImmerTai can enhance the learning efficiency by up to 17.4% and the learning quality by up to 32.3%.

KW - Immersive education

KW - Motion training

KW - VR

UR - https://www.scopus.com/pages/publications/85018368615

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85018368615&origin=recordpage

U2 - 10.1109/VR.2017.7892299

DO - 10.1109/VR.2017.7892299

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781509066476

SP - 307

EP - 308

BT - Proceedings - IEEE Virtual Reality

PB - IEEE Computer Society

T2 - 19th IEEE Virtual Reality, VR 2017

Y2 - 18 March 2017 through 22 March 2017

ER -

Immersive and collaborative Taichi motion learning in various VR environments

Abstract

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UN SDGs

Research Keywords

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