Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Analysis of VR sickness and gait parameters during non-isometric virtualwalking with large translational gain|
|Citation:||Proceedings VRCAI 2019: 17th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry, 2019 / pp.16-1-16-10|
|Publisher:||Association for Computing Machinery|
|Conference Name:||ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry (VRCAI) (17 Nov 2019 - 20 Nov 2019 : Brisbane, Australia)|
|Carlos A. Tirado Cortes, Hsiang-Ting Chen, Chin-Teng Lin|
|Abstract:||The combination of room-scale virtual reality and non-isometric virtual walking techniques is promising-the former provides a comfortable and natural VR experience, while the latter relaxes the constraint of the physical space surrounding the user. In the last few decades, many non-isometric virtual walking techniques have been proposed to enable unconstrained walking without disrupting the sense of presence in the VR environment. Nevertheless, many works reported the occurrence of VR sickness near the detection threshold or after prolonged use. There exists a knowledge gap on the level of VR sickness and gait performance for amplified non-isometric virtual walking at well beyond the detection threshold. This paper presents an experiment with 17 participants that investigated VR sickness and gait parameters during non-isometric virtual walking at large and detectable translational gain levels. The result showed that the translational gain level had a significant effect on the reported sickness score, gait parameters, and center of mass displacements. Surprisingly, participants who did not experience motion sickness symptoms at the end of the experiment adapted to the non-isometric virtual walking well and even showed improved performance at a large gain level of 10x.|
|Keywords:||Virtual Reality; Cybersickness; Locomotion; Walking; Navigation; Redirected Walking|
|Rights:||© 2019 Association for Computing Machinery.|
|Appears in Collections:||Computer Science publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.