Abstract
It is a well-known fact that virtual reality (VR) sickness is an obstacle to an immersive VR experience, however, an objective analysis of the physiological responses for VR sickness has been insufficient. In this study, our analysis uncovers how the users' visual attention varies with the level of VR sickness and how the level of VR sickness influences the center-bias tendency. Toward this, we first conduct a large-scale eye-tracking experiment of 21 inexperienced users while they experience VR sickness-oriented database VR-SP [15]. Then, we quantify the tendency of visual behavior according to VR sickness. To do this, we newly define a visual entropy measurement of VR visual attention. The experimental results clearly suggest that the center-bias effect becomes stronger as the degree of VR sickness increases. In other words, this implies that the users' explorativeness in VR content may be restricted by the VR sickness and this leads to the restraint of the immersive experience. For a more clear demonstration, we also show the visual entropy can be used to predict VR sickness with an accuracy of 80% on the VR-SP database.
Original language | English |
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Title of host publication | 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1465-1469 |
Number of pages | 5 |
ISBN (Electronic) | 9789881476890 |
Publication status | Published - 2021 |
Event | 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan Duration: 2021 Dec 14 → 2021 Dec 17 |
Publication series
Name | 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings |
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Conference
Conference | 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 |
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Country/Territory | Japan |
City | Tokyo |
Period | 21/12/14 → 21/12/17 |
Bibliographical note
Funding Information:This work was supported by Institute of Information communications Technology Planning & Evaluation (IITP).
Funding Information:
This work was supported by Institute of Information communications TechnologyPlanning & Evaluation(IITP) grant funded by the Korea government (MSIT) (No.2021-0-00352, Development of Content Quality Test Method Based on Descriptive Sensible Experience Prediction Model)
Publisher Copyright:
© 2021 APSIPA.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Signal Processing
- Instrumentation