Use of physiological signals to predict cybersickness

Dennison, Mark; Wisti, Andrew; D’Zmura, Michael

doi:10.1016/j.displa.2016.07.002

Cited by 252 publications

(194 citation statements)

References 52 publications

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“…Although it is unclear if these physiological correlates are causally related to the qualitative experience of CS, it is thought that physiological characteristics of CS may represent the process of sensory conflict reduction (Harm, 2002;Welch, 2002). Physiological measurements such as electroencephalography (EEG) and electrocardiography (ECG) demonstrate predictive validity for self-reported CS scores (Dennison et al, 2016;Kim et al, 2005). However, these measures are typically obtained during VR exposure when CS symptoms have already emerged.…”

Section: Additional Factorsmentioning

confidence: 99%

“…We selected the components to be entered into the regression model based on their correlation with ΔCS scores, with a pre-determined criterion value of r = 0.20 (e.g., Dennison et al, 2016;Kim et al, 2005). Predictors were entered into the model simultaneously to avoid the problems of stepwise regression techniques (Stephens et al, 2005).…”

Section: Multiple Regression Analysismentioning

confidence: 99%

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Estimating the sensorimotor components of cybersickness

Weech

Varghese

Barnett‐Cowan

2018

Preprint

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The user base of the virtual reality (VR) medium is growing, and many of these users will experience cybersickness. Accounting for the vast inter-individual variability in cybersickness forms a pivotal step in solving the issue. Most studies of cybersickness focus on a single factor (e.g., balance, sex, vection), while other contributors are overlooked. Here, we characterize the complex relationship between cybersickness and several indices of sensorimotor processing. In a single session, we conducted a battery of tests of balance control, vection responses, and vestibular sensitivity to self-motion. A principal components regression model, primarily composed of balance control measures during vection, significantly predicted 37% of the variability in cybersickness measures. We observed strong, inverse associations between measures of sway and cybersickness. The results reiterate that the relationship between balance control and cybersickness is anything but straightforward. We discuss other factors that may account for the remaining variance in cybersickness.

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Section: Additional Factorsmentioning

confidence: 99%

Section: Multiple Regression Analysismentioning

confidence: 99%

Estimating the sensorimotor components of cybersickness

Weech

Varghese

Barnett‐Cowan

2018

Preprint

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“…Dennison et al [25] suggested by their experiment that individuals who tend to hold their breath during HMD do not feel as ill. At present, there is no clear evidence that subjects in our experiment held their breath during the experiment.…”

Section: Discussionmentioning

confidence: 99%

The Effect of a Bio-signal Interactive Virtual Reality on Subjective Impression - A Preliminary Study

Kalansooriya

Mitsui

Takaki

et al. 2016

IJAE

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Abstract:We developed a bio-signal interactive virtual reality (VR) system to investigate the impression of users as a pilot study. In this study, three different VR scenes were given to the subjects of 20 healthy university students: bonfire (regulated with Electrocardiogram), firefly and butterfly (regulated with respiration signal). The subjective outcomes were assessed using the visual analogue scale. For the purpose of comparison, each subject also experienced the same scenes regulated with sinewave and random signals. The experiment was conducted by within-subject design: each subjects experienced three VR scenes and three input signals (bio-signal, sinewave, and random) in a counter-balanced order. As a result of the experiment, bio-signal interactive VRs can be observed as most comfortable and natural for subjects compared with the sinewave and random conditions.

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“…A prevalent alternative to the use of questionnaires is collecting physiological measurements. Skin temperature, heart rate, electrogastrogram, electrocardiogram, electrooculogram, galvanic skin response, photoplethysmogram, and breathing rate have been utilized to estimate cybersickness …”

Section: Introductionmentioning

confidence: 99%

“…Skin temperature, heart rate, electrogastrogram, electrocardiogram, electrooculogram, galvanic skin response, photoplethysmogram, and breathing rate have been utilized to estimate cybersickness. 13,14 In this work, our goal is to investigate physiological and affective responses of users while experiencing VEs that are designed to evoke varying emotions and degrees of cybersickness. For this purpose, a suite of virtual environments with distinct contexts were created.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive study of the affective and physiological responses induced by dynamic virtual reality environments

Cebeci

Çelikcan

Çapın

2019

Computer Animation & Virtual

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Previous studies showed that virtual reality (VR) environments can affect emotional state and cause significant changes in physiological responses. Aside from these effects, inadvertently induced cybersickness is a notorious problem faced in VR. In this study, to further investigate the effects of virtual environments (VEs) with different context, three dynamic VEs were created. Each VE had a particular purpose: evoking no emotion in Campfire (CF), unpleasant emotions in Hospital (HH), and cybersickness symptoms in Roller Coaster (RC). We made use of objective measurements of physiological responses such as pupil dilation, blinks, fixations, saccades, and heart rate, as well as subjective self‐assessments via pre‐ and post‐VE session questionnaires. While previous studies investigate different subsets of these measures, our study makes a comprehensive analysis of them jointly in dynamic VEs. The results of the study indicate that cybersickness produced higher saccade mean speed, whereas unpleasant context caused higher fixation count, saccade rate, and pupil dilation. Moreover, CF decreased anxiety, whereas HH and RC increased it and they also decreased comfort. Participants felt cybersickness in all VEs even in CF which is designed to minimize the effects.

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Use of physiological signals to predict cybersickness

Cited by 252 publications

References 52 publications

Estimating the sensorimotor components of cybersickness

Estimating the sensorimotor components of cybersickness

The Effect of a Bio-signal Interactive Virtual Reality on Subjective Impression - A Preliminary Study

A comprehensive study of the affective and physiological responses induced by dynamic virtual reality environments

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