A Knowledge Discovery in Motion Sickness: A Comprehensive Literature Review

Koohestani, Afsaneh; Nahavandi, Darius; Asadi, Houshyar; Kebria, Parham M.; Khosravi, Abbas; Alizadehsani, Roohallah; Nahavandi, Saeid

doi:10.1109/access.2019.2922993

Cited by 62 publications

(38 citation statements)

References 113 publications

(200 reference statements)

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“…Previous studies on blink rate and cybersickness did reveal a positive correlation between SSQ Scores and blinking [Koohestani et al 2019]. Kim et al [Kim et al 2005] described the significant changes of blink rate over time during a VR navigation task.…”

Section: Cybersickness and Blink Ratesmentioning

confidence: 99%

Eye Thought You Were Sick! Exploring Eye Behaviors for Cybersickness Detection in VR

Lopes

Tian

Boulic

2020

Motion, Interaction and Games

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Cybersickness induced through Virtual Reality (VR) applications is still one of its main barriers as it can induce unwanted sideeffects in players, significantly hindering the overall experience. Despite the wealth of research available on this topic, it is still an unsolved problem. Although previous studies have explored methods of cybersickness mitigation in addition to correlating physiological factors, there has been little research on the potential correlation of eye behavior and cybersickness. Thanks to advances in eye-tracking technology within HMDs, detecting eye behavior has become a much easier process. This paper explores the differences of pupil position and blink rate in relation to cybersickness intensity. The latter is measured through the standard and a customized version of the Simulator Sickness Questionnaire (SSQ). A total of 34 participant data was collected from two separate playing sessions of a VR maze game, where each session presented a unique control scheme.

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Section: Cybersickness and Blink Ratesmentioning

confidence: 99%

Eye Thought You Were Sick! Exploring Eye Behaviors for Cybersickness Detection in VR

Lopes

Tian

Boulic

2020

Motion, Interaction and Games

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“…Eye movements and body temperature have been claimed the most reliable objective measures of CS symptoms in comparison to other physiological signals [42]. This is especially pertinent given that consumer VR with built-in eye-tracking functions (e.g., Vive Pro Eye) have become available in the market.…”

Section: ) Multimodal Objective Measurementmentioning

confidence: 99%

A Review of Electrostimulation-based Cybersickness Mitigations

McGill

Brewster

et al. 2020

2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

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With the development of consumer virtual reality (VR), people have increasing opportunities to experience cybersickness (CS) -a kind of visuallyinduced motion sickness (MS). In view of the importance of CS mitigation (CSM), this paper reviews the methods of electrostimulation-based CSM (e-CSM), broadly categorised as either "VR-centric" or "Human-centric". "VR-centric" refers to approaches where knowledge regarding the visual motion being experienced in VR directly affects how the neurostimulation is delivered, whereas "Human-centric" approaches focus on the inhibition or enhancement of human functions per se without knowledge of the experienced visual motion. We found that 1) most e-CSM approaches are based on visual-vestibular sensory conflict theoryone of the generally-accepted aetiologies of MS, 2) the majority of e-CSM approaches are vestibular system-centric, either stimulating it to compensate for the mismatched vestibular sensory responses, or inhibiting it to make an artificial and temporary dysfunction in vestibular sensory organs or cortical areas, 3) Vestibular sensory organbased solutions are able to mitigate CS with immediate effect, while the real-time effect of vestibular cortical areas-based methods remains unclear, due to limited public data, 4) Based on subjective assessment, VRcentric approaches could relieve all three kinds of symptoms (nausea, oculomotor, and disorientation), which appears superior to the human-centric ones that could only alleviate one of the symptom types or just have an overall relief effect. Finally, we propose promising future research directions in the development of e-CSM.

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“…Relationships between the levels of MS experienced by subjects have been demonstrated in various EEG, EGG, and eye movement studies. Koohestani et al (2019) give an overview of objective biosignal measures in MS research. Objective kinematic measures such as center of pressure (COP) are also documented as having relationships to MS levels in the literature ( Thurrell and Bronstein, 2002 ; Weech et al, 2018 ) as well as spectral characteristics of spontaneous sway, which have been measured as a possible objective measurement for a predictive MS parameter Laboissière et al (2015) .…”

Section: Introductionmentioning

confidence: 99%

Toward Predicting Motion Sickness Using Virtual Reality and a Moving Platform Assessing Brain, Muscles, and Heart Signals

Recenti¹,

Ricciardi²,

Aubonnet³

et al. 2021

Front. Bioeng. Biotechnol.

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Motion sickness (MS) and postural control (PC) conditions are common complaints among those who passively travel. Many theories explaining a probable cause for MS have been proposed but the most prominent is the sensory conflict theory, stating that a mismatch between vestibular and visual signals causes MS. Few measurements have been made to understand and quantify the interplay between muscle activation, brain activity, and heart behavior during this condition. We introduce here a novel multimetric system called BioVRSea based on virtual reality (VR), a mechanical platform and several biomedical sensors to study the physiology associated with MS and seasickness. This study reports the results from 28 individuals: the subjects stand on the platform wearing VR goggles, a 64-channel EEG dry-electrode cap, two EMG sensors on the gastrocnemius muscles, and a sensor on the chest that captures the heart rate (HR). The virtual environment shows a boat surrounded by waves whose frequency and amplitude are synchronized with the platform movement. Three measurement protocols are performed by each subject, after each of which they answer the Motion Sickness Susceptibility Questionnaire. Nineteen parameters are extracted from the biomedical sensors (5 from EEG, 12 from EMG and, 2 from HR) and 13 from the questionnaire. Eight binary indexes are computed to quantify the symptoms combining all of them in the Motion Sickness Index (IMS). These parameters create the MS database composed of 83 measurements. All indexes undergo univariate statistical analysis, with EMG parameters being most significant, in contrast to EEG parameters. Machine learning (ML) gives good results in the classification of the binary indexes, finding random forest to be the best algorithm (accuracy of 74.7 for IMS). The feature importance analysis showed that muscle parameters are the most relevant, and for EEG analysis, beta wave results were the most important. The present work serves as the first step in identifying the key physiological factors that differentiate those who suffer from MS from those who do not using the novel BioVRSea system. Coupled with ML, BioVRSea is of value in the evaluation of PC disruptions, which are among the most disturbing and costly health conditions affecting humans.

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A Knowledge Discovery in Motion Sickness: A Comprehensive Literature Review

Cited by 62 publications

References 113 publications

Eye Thought You Were Sick! Exploring Eye Behaviors for Cybersickness Detection in VR

Eye Thought You Were Sick! Exploring Eye Behaviors for Cybersickness Detection in VR

A Review of Electrostimulation-based Cybersickness Mitigations

Toward Predicting Motion Sickness Using Virtual Reality and a Moving Platform Assessing Brain, Muscles, and Heart Signals

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