Reducing Cybersickness

Kemeny, Andras; Chardonnet, Jean-Rémy; Colombet, Florent

doi:10.1007/978-3-030-59342-1_4

Getting Rid of Cybersickness 2020

DOI: 10.1007/978-3-030-59342-1_4

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Reducing Cybersickness

Andras Kemeny¹,

Jean-Rémy Chardonnet²,

Florent Colombet³

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2021

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Cited by 2 publications

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“…More broadly, the issue of prediction of VIMS is being approached from several perspectives by researchers (Kemeny et al, 2020). The attempts to predict sickness utilizing a variety of subjective, physiological measures, and/or kinematic measures have yielded a number of viable prediction models (Dennison et al, 2019; Kim et al, 2005; Weech et al, 2019).…”

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confidence: 99%

Using Nonlinear Kinematic Parameters as a Means of Predicting Motion Sickness in Real-Time in Virtual Environments

et al. 2021

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Objective This article presents two studies (one simulation and one pilot) that assess a custom computer algorithm designed to predict motion sickness in real-time. Background Virtual reality has a wide range of applications; however, many users experience visually induced motion sickness. Previous research has demonstrated that changes in kinematic (behavioral) parameters are predictive of motion sickness. However, there has not been research demonstrating that these measures can be utilized in real-time applications. Method Two studies were performed to assess an algorithm designed to predict motion sickness in real-time. Study 1 was a simulation study that used data from Smart et al. (2014). Study 2 employed the algorithm on 28 new participants’ motion while exposed to virtual motion. Results Study 1 revealed that the algorithm was able to classify motion sick participants with 100% accuracy. Study 2 revealed that the algorithm could predict if a participant would become motion sick with 57% accuracy. Conclusion The results of the present study suggest that the motion sickness prediction algorithm can predict if an individual will experience motion sickness but needs further refinement to improve performance. Application The algorithm could be used for a wide array of VR devices to predict likelihood of motion sickness with enough time to intervene.

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Using Nonlinear Kinematic Parameters as a Means of Predicting Motion Sickness in Real-Time in Virtual Environments

et al. 2021

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Virtual reality expands the toolkit for conducting health psychology research

Martingano

Persky

2021

Social & Personality Psych

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Virtual reality (VR) has become a readily available consumer technology, strengthening its promise as a research tool for health psychology. We identify five key strengths of VR‐based research: data collection, realism, experimental control, adaptability, and mobility (DREAM). We review how these advantages allow researchers to investigate behavioral, psychological, and social processes related to health and well‐being in novel ways, by using VR as both a stimulus and a measurement tool. We also describe challenges facing VR research and potential strategies researchers can use to mitigate them. In addition to reviewing existing research, we hope to inspire researchers to consider ways in which VR might be used in future to augment their own research programs or answer currently impracticable research questions in health psychology.

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Reducing Cybersickness

Cited by 2 publications

References 130 publications

Using Nonlinear Kinematic Parameters as a Means of Predicting Motion Sickness in Real-Time in Virtual Environments

Using Nonlinear Kinematic Parameters as a Means of Predicting Motion Sickness in Real-Time in Virtual Environments

Virtual reality expands the toolkit for conducting health psychology research

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