Jeremy Albert
Master of Science Thesis Project, November 2017
Paper: J. Albert(+) and K. Sung. “User-centric Classification of Virtual Reality Locomotion,” in Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology, VRST 2018, Tokyo, Japan, November 28 – December 01, 2018, pp. 127:1–127:2. Poster | Conference-Presentation
The recent introduction of consumer grade virtual reality (VR) systems caused a renewed interest in the technology. However, virtual reality locomotion (VRL) remains a significant issue for virtual reality. VRL is traveling in an infinite-scale virtual world, while remaining in the confines of a room-scale real world. VRL remains an issue because of three fundamental challenges, sickness, presence, and fatigue. Sickness occurs when there is a conflict between a user’s vestibular and visual senses; Presence occurs when a user feels they are truly in the virtual world; Fatigue occurs when a user expends muscular energy for a sustained period.
This thesis proposes the User-Centric Classification (UCC) framework for discussing, comparing, and identifying characteristics of VRL methods. The UCC framework classifies VRL methods according to three metrics, sickness, presence, and fatigue, where each metric is determined by how well a VRL method addresses the corresponding challenge. Results from previous VRL research, were analyzed in the UCC framework. Analysis indicated current methods were designed to deliver performance in one of the challenges by sacrificing performance in the other two.
To test the validity and strengths of the UCC framework, we implemented the VRL methods of controller, teleport, and walking in a common testbed. We designed the testbed to expose the user to the three VRL challenges and developed a set of questionnaires to capture the user experience. Test results from 30 users showed that the three VRL methods produce different UCC metrics and that we can capture these differences. Contrasting the three methods with respect to the three VRL challenges allows us to discuss and compare methods in a meaningful way and to examine UCC metric trade-offs while searching for an ideal VRL method.