https://orcid.org/0000-0002-0635-6054
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Abstract
Users’ motion representation in virtual reality (VR) can be modulated visually by introducing a mismatch with their real motion, which can bring benefits to exercise and rehabilitation and has great potential for exergame applications in VR. Users’ experience of control is a critical consideration for user experience in human–computer interaction and should be paid special attention when movement modulation is implemented in VR. However, how movement modulation affects users’ experience of control and motor performance has not been fully investigated in detail. This research included 49 participants and investigated how the experience of control is influenced by reaching movement modulation in two types: the enhancement and reduction modes. Different modulation modes were designed to study their influence on the explicit experience of control in self-ratings and the implicit measured experience of control in intentional binding and electroencephalography. Participants’ movement trajectory, velocity, and completion time were analyzed for motor performance. The results illustrate a significant effect of movement modulation on the users’ motor performance and experience of control in self-ratings and EEG. This study makes a major contribution through a comprehensive analysis of the experience of control with movement modulation and provides important and practical design considerations on movement modulation design in future exercise-based applications with positive controlling experiences in VR.
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This article has been republished with minor changes. These changes do not impact the academic content of the article.
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Notes on contributors
Liu Wang
Liu Wang received the MEng degree in industrial design from the Beijing Institute of Technology, Beijing, China, in 2019. She is currently working toward a PhD at the University of Liverpool, Liverpool, UK. Her research interests include human–computer interaction and user experience in virtual reality.
Mengjie Huang
Mengjie Huang is an Assistant Professor in the Design School at Xi’an Jiaotong-Liverpool University, China. She received a PhD degree from National University of Singapore. Her research interests lie in human–computer interaction design, with special focuses on human factors, user experience, and brain decoding.
Rui Yang
Rui Yang is an Associate Professor in the School of Advanced Technology at Xi’an Jiaotong-Liverpool University, China. He received a BEng degree in Computer Engineering and a PhD degree in Electrical and Computer Engineering from National University of Singapore. His research interests include machine learning-based data analysis and applications.
Chengxuan Qin
Chengxuan Qin received the MRes degree in Pattern Recognition and Intelligent Systems from the University of Liverpool, in 2023. His research interests include brain-computer interfaces, temporal signal analysis, and machine learning.
Ji Han
Ji Han is a Senior Lecturer (Associate Professor) in Design and Innovation at the Department of Innovation, Technology, and Entrepreneurship at the University of Exeter. His research addresses various topics relating to design and creativity. His general interests include design creativity, data-driven design, AI in design, and virtual reality.
Hai-Ning Liang
Hai-Ning Liang is a Professor in the Department of Computing at Xi’an Jiaotong-Liverpool University, China. His research interests fall within human–computer interaction, focusing on developing novel interactive techniques and applications for virtual/augmented reality, gaming, visualization, and learning systems.