Yulong Bian

Dr. Yulong Bian

orcid

Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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20 April 2020 in IEEE Access
Verified
Journal Article
Open access
CZChao Zhou
Chao Zhou
HLHong Li
Hong Li
YBYulong Bian
Yulong Bian
The purpose of this study was to test different 3D display technologies in a hands-on virtual experiential learning environment (VELE) and determine the optimal 3D display technology. A conceptual framework was firstly proposed to explain the mechanism of how VR display features affect virtual experiential learning. Then, a single-factor within-subject experimental design was adopted for testing. The within-subject factor was three types of 3D display technology: fully immersive virtual reality (VR) mode [VR head-mounted display (HMD)], partially immersive VR mode (3D projection), and augmented reality (AR) mode (AR HMD). The dependent variables were visual comfort, interaction experience, learning experience, and outcome. A virtual math learning environment was established, and the aforementioned display technologies were tested in two hands-on virtual experiential learning scenarios. Results showed that different display technologies significantly affected users’ visual comfort, interaction experience, learning experience, and outcome in experiential learning (ps<0.05). User ratings on these aspects for the VR HMD were significantly higher than those for the 3D projection and AR HMD. Thus, the VR HMD contributes to a best viewing experience and learning experience in terms of hands-on virtual experiential learning in the scenarios tested. Whether the study results still hold reliably in terms of more complex learning activities and long-term learning needs to be further studied.

17 August 2023 in IEEE Transactions on Affective Computing
Verified
Journal Article
Closed access
YBYulong Bian
Yulong Bian
CZChao Zhou
Chao Zhou
YZYang Zhang
Yang Zhang
JLJuan Liu
Juan Liu
JSJenny Sheng
Jenny Sheng
YLYong-Jin Liu
Yong-Jin Liu
Exploring effective approaches to enhance face-to-face interactions and interpersonal relationships is an important topic in the applications of affective computing. According to the co-actualization model, we propose a face-to-face co-participation serious game for relationship enhancement, with a focus on battling COVID-19. Moreover, a prototype system is developed using an immersive virtual environment and a low-cost brain-computer interface. Through this system, a dynamic flow experience enhancement tool is utilized to involve partners in the cooperative task. To evaluate the system performance, two studies are conducted with schoolmates as participants. Study 1 compares the cooperative and competitive modes, and demonstrates that the former elicited higher level of decision-making challenge and affections, which are beneficial for forming relationships. Study 2 further examines the effect of the dynamic flow enhancement tool in the cooperative task and the results show its effectiveness in promoting flow experience, perceived closeness, and intimacy in relationships. Given this short-term participation, participants felt a greater sense of closeness and intimacy than they had before the test. In conclusion, our proposed system is effective in enhancing schoolmate relationships.

10 February 2022 in Virtual Reality
Verified
Journal Article
Closed access
PDF file
YBYulong Bian
Yulong Bian
CZChao Zhou
Chao Zhou
JLJuan Liu
Juan Liu
WGWenxiu Geng
Wenxiu Geng
YSYing Shi
Ying Shi

12 March 2024 in IEEE Transactions on Visualization and Computer Graphics
Verified
Journal Article
Closed access
WLWeiying Liu
Weiying Liu
YZYanyan Zhang
Yanyan Zhang
BZBaiqiao Zhang
Baiqiao Zhang
QXQianqian Xiong
Qianqian Xiong
HZHong Zhao
Hong Zhao
YBYulong Bian
Yulong Bian
Children diagnosed with Autism Spectrum Disorder (ASD) often exhibit motor disorders. Dance Movement Therapy (DMT) has shown great potential for improving the motor control ability of children with ASD. However, traditional DMT methods often lack vividness and are difficult to implement effectively. To address this issue, we propose a Mixed Reality DMT approach, utilizing interactive virtual agents. This approach offers immersive training content and multi-sensory feedback. To improve the training performance of children with ASD, we introduce a novel training paradigm featuring a self-guided mode. This paradigm enables the rapid creation of a virtual twin agent of the child with ASD using a single photo to embody oneself, which can then guide oneself during training. We conducted an experiment with the participation of 24 children diagnosed with ASD (or ASD propensity), recording their training performance under various experimental conditions. Through expert rating, behavior coding of training sessions, and statistical analysis, our findings revealed that the use of the twin agent for self-guidance resulted in noticeable improvements in the training performance of children with ASD. These improvements were particularly evident in terms of enhancing movement quality and refining overall target-related responses. Our study holds clinical potential in the field of medical treatment and rehabilitation for children with ASD.

6 October 2021 in IEEE Transactions on Games
Verified
Journal Article
Closed access
HYHui Yang
Hui Yang
JLJunyan Li
Junyan Li
JLJingnan Liu
Jingnan Liu
YBYulong Bian
Yulong Bian
JLJuan Liu
Juan Liu
The COVID-19 has led to home quarantine in global scale, which increased people’s need for indoor fitness activities in order to relieve stress/anxiety and learn knowledge well during the prevention. In this article, an augmented reality (AR) indoor exergame is developed, mainly a kind of moving target hitting task with Lomachenkos headwear speedball as the prototype. Then, a comparative experiment is performed to test the effect of the exergame in relieving anxiety, as well as the differences between AR and virtual reality modes (using HTC Vive Pro) on user experience (flow experience and motion sickness) and interaction efficiency. Results from 28 participants show that the anxiety level of participants significantly reduced after using the exergame. Although the difference on flow experience is not significant, there is a marginally significant difference on nausea level, a dimension of motion sickness. Moreover, interaction efficiency between the two modes is significantly different. In the task of moving target hitting, AR mode performed better in avoiding motion sickness and improving interaction performance.

9 July 2024 in International Journal of Human-Computer Studies
Verified
Journal Article
Closed access
YLYang Li
Yang Li
JLJuan Liu
Juan Liu
JHJin Huang
Jin Huang
YZYang Zhang
Yang Zhang
XPXiaolan Peng
Xiaolan Peng
FTFeng Tian
Feng Tian