VR Sickness Adaptation With Ramped Optic Flow Transfers From Abstract To Realistic Environments

Abstract

VR sickness is a major concern for many users as VR continues its expansion towards widespread everyday use. VR sickness is thought to arise, at least in part, due to the user’s intolerance of conflict between the visually simulated self-motion and actual physical movement. Many mitigation strategies involve consistently modifying the visual stimulus to reduce its impact on the user, but this individualized approach can have drawbacks in terms of complexity of implementation and non-uniformity of user experience. This study presents a novel alternative approach that involves training the user to better tolerate the adverse stimulus by tapping into natural adaptive perceptual mechanisms. In this study, we recruited users with limited VR experience that reported susceptibility to VR sickness. Baseline sickness was measured as participants navigated a rich and naturalistic visual environment. Then, on successive days, participants were exposed to optic flow in a more abstract visual environment, and strength of the optic flow was successively increased by increasing the visual contrast of the scene, because strength of optic flow and the resulting vection are thought to be major causes of VR sickness. Sickness measures decreased on successive days, indicating that adaptation was successful. On the final day, participants were again exposed to the rich and naturalistic visual environment, and the adaptation was maintained, demonstrating that it is possible for adaptation to transfer from more abstract to richer and more naturalistic environments. These results demonstrate that gradual adaptation to increasing optic flow strength in well-controlled, abstract environments allows users to gradually reduce their susceptibility to sickness, thereby increasing VR accessibility for those prone to sickness.

Gender differences in cybersickness: Clarifying confusion and identifying paths forward

“…It has been established that cybersickness can be reduced through repeated exposure to VR [1,19,34], referred to as adaptation. In a recent study with a very large participant sample (n = 837; [28]), VR experience was negatively related to cybersickness, although the relationship was modest (r = -.15).…”

“…Examples of stimulus-based factors that affect sickness include the content (e.g., games, 3D videos [38]), the locomotion interface (e.g., joystick, teleporting [6,8,27,51]), exposure duration [45], and task workload [40]. Examples of individual factors include prior VR experience [10,19,28], motion sickness history [10,25,45], and gender 1 [10,28,45].…”

“…For example, the VR content would moderate the gender difference in cybersickness if the gender difference were exaggerated when playing VR games compared to other types of VR simulation. The 1 We use the term gender throughout this paper, but the literature does not consistently distinguish between gender and sex. Therefore, it is unclear whether cybersickness is affected by biological factors (i.e., sex), experiencebased factors (i.e., gender), or both.…”

Digitalising qualitative social research? On the potential of digital features to enhance data collection in qualitative research: the example of a virtual reality serious game in a qualitative research project

“…In general, as a stepwise adaptation to IVR (Adhanom et al, 2022) (which is possible in specific experimental research environments) is not feasible, it is recommended that self-movement through virtual realities takes place at low speed, as the speed at which objects move through a scene is related to the occurrence of motion sickness. This also applies to unexpected and surprising movement manoeuvres, and to unforeseen events, such as visual frights that participants do not expect and were not informed about (Behr et al, 2005).…”

Gender differences in cybersickness: Clarifying confusion and identifying paths forward

“…Examples of stimulus-based factors that affect sickness include the content (e.g., games, 3D videos [38]), the locomotion interface (e.g., joystick, teleporting [6,8,27,51]), exposure duration [45], and task workload [40]. Examples of individual factors include prior VR experience [10,19,28], motion sickness history [10,25,45], and gender 1 [10,28,45].…”