CHS: Medium: Prediction, Early Detection, and Mitigation of Virtual Reality Simulator Sickness

University Of Minnesota-Twin Cities, Minneapolis MN

Investigators

Abstract

With a global installed user base of over 28 million people, virtual reality is a rapidly advancing field with numerous emerging applications in education, training, rehabilitation, healthcare, social communications, and entertainment. However, the effectiveness of virtual reality applications and their rate of public adoption is currently limited by the fact that many users experience physical discomfort during or after their use, with symptomatic characteristics indicative of motion sickness. This problem, known as "simulator sickness" or "cybersickness", is one of the most significant usability challenges for users, developers, and stakeholders of immersive technologies. This project offers a novel and empirically-grounded research methodology to study, predict, detect, and ultimately mitigate simulator sickness, which can substantially improve both the subjective user experience and the effectiveness of current and future virtual reality applications. Furthermore, prior research has shown that motion sickness disproportionately affects women. This project seeks to advance understanding of these differences and develop adaptive strategies for mitigating simulator sickness on an individual level, which can ultimately increase the overall number of potential users worldwide and erode the inequitable barriers that currently exist for engaging with immersive technologies. This project seeks to address simulator sickness through a systematic effort that will advance fundamental understanding of the relationship between motion kinematics and the adverse symptoms commonly experienced by users of virtual reality systems. Specific activities include the following: (1) development of models that predict the likelihood of experiencing simulator sickness based on an individual's motion characteristics; (2) introduction of real-time methods for early detection of sickness onset before the user experiences discomfort; (3) identification of specific problematic virtual reality stimuli that are associated with simulator sickness; (4) development of adaptive mitigation strategies to reduce the likelihood and severity of adverse symptoms; and (5) rigorous experimental evaluation of the effectiveness and tradeoffs of newly developed techniques. The project offers methodological innovation in several areas related to the fundamental study of motion sickness phenomena, including the investigation of eye gaze stability for the prediction or early detection of simulator sickness. Additionally, a key innovation is that the data collected from empirical studies will be utilized to develop adaptive techniques that adjust automatically based on the individual's predicted sickness levels and current real-time state, both measured through quantitative motion kinematics. The project will also result in the creation of a large-scale motion kinematics dataset that will be made publicly available for future research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.