Automatic Detection and Prediction of Cybersickness Severity using Deep Neural Networks from user’s Physiological Signals

Islam, Rifatul; Lee, Yonggun; Jaloli, Mehrad; Muhammad, Imtiaz; Zhu, Dakai; Rad, Paul; Huang, Yufei; Quarles, John

doi:10.1109/ismar50242.2020.00066

Cited by 48 publications

(46 citation statements)

References 53 publications

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“…Lin et al [ 32 ] quantified cybersickness into three stages by categorizing the power of EEG and electro-oculography from 25 volunteers who participated in a

VR experiment. Similarly, Islam et al [ 33 ] quantified cybersickness severity using a neural network based on 31 participants’ physiological signals. In the most advanced study, Lin et al [ 34 ] predicted the level of motion sickness from the EEG spectrum.…”

Section: Introductionmentioning

confidence: 99%

Cybersickness and Its Severity Arising from Virtual Reality Content: A Comprehensive Study

Son

2022

Sensors

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Virtual reality (VR) experiences often elicit a negative effect, cybersickness, which results in nausea, disorientation, and visual discomfort. To quantitatively analyze the degree of cybersickness depending on various attributes of VR content (i.e., camera movement, field of view, path length, frame reference, and controllability), we generated cybersickness reference (CYRE) content with 52 VR scenes that represent different content attributes. A protocol for cybersickness evaluation was designed to collect subjective opinions from 154 participants as reliably as possible in conjunction with objective data such as rendered VR scenes and biological signals. By investigating the data obtained through the experiment, the statistically significant relationships—the degree that the cybersickness varies with each isolated content factor—are separately identified. We showed that the cybersickness severity was highly correlated with six biological features reflecting brain activities (i.e., relative power spectral densities of Fp1 delta, Fp 1 beta, Fp2 delta, Fp2 gamma, T4 delta, and T4 beta waves) with a coefficient of determination greater than 0.9. Moreover, our experimental results show that individual characteristics (age and susceptibility) are also quantitatively associated with cybersickness level. Notably, the constructed dataset contains a number of labels (i.e., subjective cybersickness scores) that correspond to each VR scene. We used these labels to build cybersickness prediction models and obtain a reliable predictive performance. Hence, the proposed dataset is supposed to be widely applicable in general-purpose scenarios regarding cybersickness quantification.

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“…Lin et al [ 32 ] quantified cybersickness into three stages by categorizing the power of EEG and electro-oculography from 25 volunteers who participated in a

Section: Introductionmentioning

confidence: 99%

Cybersickness and Its Severity Arising from Virtual Reality Content: A Comprehensive Study

Son

2022

Sensors

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“…Additionally, what needs to be emphasized is that the included factors are not fully equal to the inputs from the prediction model. For example, Islam et al (2020) used both the FMS score and FMS-labelled physiological signals as inputs for training the model but considered different VR content during recording. A majority of nine papers used content-related factors, especially content type, scene movement, optic flow map, as inputs to the model.…”

Section: Prediction Of Cybersicknessmentioning

confidence: 99%

“…Generally, including a variety of factors and features could achieve high accuracy of prediction as in Jin et al (2018), Porcino et al (2020). Furthermore, providing scene movement solely with a significant sample size might also achieve a good result as in Islam et al (2020), Lee et al (2019). In contrast, prediction with a limited sample size tends to have reduced power as in Padmanaban et al (2018), Wang et al (2021).…”

Section: More Features Allow a Better Predictionmentioning

confidence: 99%

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A review of cybersickness in head-mounted displays: raising attention to individual susceptibility

2022

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Cybersickness still poses a significant challenge to the widespread usage of virtual reality, leading to different levels of discomfort and potentially breaking the immersive experience. Researchers have attempted to discover the possible fundamental causes of cybersickness for years. Despite the longstanding interest in the research field, inconsistent results have been drawn on the contributing factors and solutions to combating cybersickness. Moreover, little attention has been paid to individual susceptibility. A consolidated explanation remains under development, requiring more empirical studies with robust and reproducible methodologies. This review presents an integrated survey connecting the findings from previous review papers and the state of the art involving empirical studies and participants. A literature review is then presented, focusing on the practical studies of different contributing factors, the pros and cons of measurements, profiles of cybersickness, and solutions to reduce this phenomenon. Our findings suggest a lack of considerations regarding user susceptibility and gender balance in between groups studies. In addition, incongruities among empirical findings raised concerns. We conclude by suggesting points of insights for future empirical investigations.

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“…heart rate) are used in prior work, e.g. for affective states [11], cybersickness [13], biometrics [24], anxiety [30], or stress levels [8]. However, the number of studies is rather limited.…”

Section: Introductionmentioning

confidence: 99%

Indicators of Training Success in Virtual Reality Using Head and Eye Movements

Gisler

Schneider

Handali

et al. 2021

2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)

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Automatic Detection and Prediction of Cybersickness Severity using Deep Neural Networks from user’s Physiological Signals

Cited by 48 publications

References 53 publications

Cybersickness and Its Severity Arising from Virtual Reality Content: A Comprehensive Study

Cybersickness and Its Severity Arising from Virtual Reality Content: A Comprehensive Study

A review of cybersickness in head-mounted displays: raising attention to individual susceptibility

Indicators of Training Success in Virtual Reality Using Head and Eye Movements

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