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Early Prediction of Cybersickness in Virtual, Augmented & Mixed Reality Applications: A Review


Abstract and Figures

The field of Virtual, Augmented & Mixed Reality (VAMR) has already taken up the consumer market by storm and are demanding among users owing to their practicality and aesthetics. Despite the massive popularity and hype, VAMR carry a loathsome side-effect called “Cybersickness”, which is often times severe enough to cause significant discomfort to the users causing the discontinuation of use. Research have been going on for years with the intentions of finding the theories and factors which contribute to this unpleasant experience but we are in no way shape to determine the underlying contrivances of cybersickness. There are systems to quantify and measure cybersickness but a symptom prediction tool which can be used by application developers to evaluate their products for cybersickness susceptibility is non-existent. This paper presents a summary of literature and provides a review and a discussion with the intentions of deriving a predictive algorithm which would ultimately address the research gap and missing links.
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Early Prediction of Cybersickness in Virtual,
Augmented & Mixed Reality Applications: A
Brion M. Silva
Department of Computing
Informatics Institute of Technology
No 57, Ramakrishna Road,
Colombo 06, Sri Lanka.
Pumudu Fernando
Department of Computing
Informatics Institute of Technology
No 57, Ramakrishna Road,
Colombo 06, Sri Lanka.
Abstract - The field of Virtual, Augmented & Mixed Reality
(VAMR) has already taken up the consumer market by storm
and are demanding among users owing to their practicality and
aesthetics. Despite the massive popularity and hype, VAMR
carry a loathsome side-effect called “Cybersickness”, which is
often times severe enough to cause significant discomfort to the
users causing the discontinuation of use. Research have been
going on for years with the intentions of finding the theories and
factors which contribute to this unpleasant experience but we
are in no way shape to determine the underlying contrivances of
cybersickness. There are systems to quantify and measure
cybersickness but a symptom prediction tool which can be used
by application developers to evaluate their products for
cybersickness susceptibility is non-existent. This paper presents
a summary of literature and provides a review and a discussion
with the intentions of deriving a predictive algorithm which
would ultimately address the research gap and missing links.
Keywords cybersickness, virtual reality, computer-vision,
Over the past few decades, the way people interact with
computers has changed drastically [1]. There had been
numerous attempts over the years to bridge the human-
computer barrier with the ultimate goal of making the human-
computer interactions as natural as possible. The most novel
idea was the thought of using hands as a device to connect and
communicate with computers mimicking human to human
interactions [2]. It is safe to say that this has been achieved
with the invention of digital reality systems also known as
Virtual, Augmented & Mixed Reality (VAMR) systems.
The field of VAMR is rapidly blooming as a result of the
massive popularity of new generation smart devices such as
displays, hand-held devices and wearables. VAMR is working
towards the reduction of cognitive load minimizing the
human-computer interaction efforts. These systems offer
immersion to the users diminishing attention shifts [3].
Despite of the popularity, there is an abundance of issues
when it comes to all three technologies. Most of these issues
are common across the platforms and research has been going
on for a long time trying to tackle them. The most prominent
problem that stands out has to be the concerns regarding the
user experience (UX). It has been discovered that VAMR
systems cause a nasty side effect called “Cybersickness”
which directly obstructs the UX forcing the user to terminate
the session instantly [4].
Cybersickness has persisted throughout the existence of
VAMR systems [5] and as a result, a large population are
unable to take advantage of this technology. Researchers are
busy finding the causes and the user response to cybersickness
but only a handful of people are working on to prevent or
predict the occurrence of cybersickness.
A wide variety of literature was analysed to gather
knowledge and insight on the symptoms, theories and
contributing factors of cybersickness. The already existing
approaches of measuring and predicting cybersickness were
critically appraised giving more priority to recently published
research. The discovered evidence will be presented in a
narrative manner through the upcoming sections.
The term “Cybersickness was proposed by McCauley
and Sharkey (1992) [6] and it was described as the interim
side-effects caused by virtual reality immersion.
Cybersickness exhibits similarities to motion sickness and
simulator sickness and it’s often categorised as a form of
motion sickness [7]. But the major discrepancy when
comparing these phenomenon is the user’s sate of motion. In
cybersickness, the user is stationary for the most part but
experiences self-motion sensations as a result of the moving
imagery [8]. This sensation is commonly referred to as
From a physiological standpoint, human body is not in a
position to cope cybersickness and according to researchers at
Coventry University, up to 80% of the population experience
some form of cybersickness [9], [10].
A. Symptoms
VAMR experiences can induce an array of symptoms
associated with cybersickness. Following are some commonly
identified side-effects and symptoms [8], [11], [12].
1. Eye strain
2. Nausea
3. Headache
4. Excessive Sweating
5. Dryness of mouth
6. Fullness of stomach
7. Vertigo
8. Ataxia
9. Vomiting
10. Dizziness
11. Disorientation
12. Fatigue
13. Strange hand eye coordination
The above symptoms can occur during and post exposure
and the majority of these side-effects are temporary whereas
few can linger for hours [13] or days [14]. As a result, many
virtual experience providers recommend users to limit
themselves from attempting risky activities such as driving for
at least an hour after exposure to VAMR.
B. Theories
Over the years, several research groups have identified
three major explanatory theories for the existence of
Sensory Conflict Theory is the oldest and the most
accepted theory among the three [15]. It claims that VAMR is
likely to cause mismatches between the vestibular and visual
inputs of the human body and these discrepancies can lead to
cybersickness related symptoms such as nausea, headaches
and other oculomotor disturbances [16], [17]. However the
theory falls short in explaining the reason for the users to be
sick due to these cue conflicts [8].
Poison/ Intoxication Theory tries to base the
explanations for the occurrence of cybersickness from an
evolutionary standpoint. According to Treisman (1977) [18],
when the human body experiences abnormal coordination of
the visual, vestibular, and other sensory inputs, the nervous
system mistakes these circumstances with poison digestion
and responds by emptying the stomach. This corporal
response of the nervous system can cause symptoms like
nausea, vomiting and discomfort.
Postural Instability Theory was developed by Riccio
and Stoffregen and it states that the human body always tries
to maintain stable posture in the environment and the
unexpected changes to the environment could result in the loss
of postural control [19].
All these theories provide compelling arguments but
further research needs to be conducted to confirm the validity
of these claims.
C. Contributing Factors
Apart from the theories discussed above, there are three
main categories of factors that influence the intensification of
cybersickness. Namely, Hardware & Software factors,
Individual Differences and Environmental Factors [8].
Hardware & Software Factors
Issues related to hardware & software had been a huge
bottleneck at the dawn of the VAMR industry but fortunately,
they have improved quite a until then [20]. But there are few
outstanding issues that are left to be addressed.
Oftentimes virtual reality (VR) tech suffers from position
tracking errors. Inconsistencies in tracking could register
unstable information resulting in jitter. Jitter has a tendency to
cause dizziness and lack of concentration [21].
Head mounted displays (HMD) usually takes a bit of time to
update users visual feed in response to the head movements.
This delay is commonly referred to as “Lag” and has a
tendency to cause unsettling sensations which result in
cybersickness symptoms [22].
Flicker is another issue and the perception of flicker will differ
from person to person and the likelihood increases with the
increase of field of view (FOV). Flicker will cause eye fatigue
[23] and could be reduced by increasing the framerate of the
visual feed.
Individual Differences
Susceptibility to cybersickness hugely depend on individual
factors such as health, behavioural conditions, age, race,
ethnicity etc. [24]. Oftentimes individual differences are
overlooked when considering contributing factors of
cybersickness but research suggests that these shouldn’t be
disregarded [25][27].
Environmental Factors
Environmental conditions such as temperature, relative
humidity, light, air quality, ambient noise etc. can aid to
cybersickness during a VAMR experience. These conditions
aren’t so easy to control due to their unpredictable nature [24].
Cybersickness is not a new phenomenon [24] and the
concerns about it had paved the way for a rich body of
literature over the years. Its studies can be dated back decades
since VAMR has a broad spectrum of applications ranging
from home entertainment to military [28]. As stated in the
above section, cybersickness has a diverse range of symptoms.
Researchers have been using a wide verity of approaches and
measures to detect and rate the severity of these cybersickness
symptoms [29]. These approaches can be mainly categorised
in to four sections.
1. Questionnaire based approaches
2. Postural Instability based approaches
3. Physiological/ Biometric approaches
4. Predictive Model based approaches
Questionnaires were the first attempts made to rate and
quantify symptoms in simulators and these approaches tend to
lean towards simulator sickness and motion sickness rather
than cybersickness. On the contrary, postural instability
related approaches were inspired by the postural instability
theory in the recent years. Some have attempted to measure
physiological signals and biometrics in order to predict the
onset of cybersickness where as others have taken a predictive
model based approach. All these four categories are explained
in detail below.
A. Questionnaire based approaches
Kennedy et al. (1993) [30] utilized a 28 symptom version
of the Pensacola Motion Sickness Questionnaire (MSQ)
which was originally developed by Kellogg et al. (1964) [31]
and conducted an extensive research by analyzing more than
1000 sets of previous data and came up with a 16 term
measurement method to quantify cybersickness (see Figure 1).
Their main objective was to demystify the similarity concept
of motion sickness and simulator sickness and to point out the
need for a distinguished index for measuring simulator
sickness severity. Potential research subjects were subjected
to the “Simulator Sickness Questionnaire” (SSQ) pre and post
exposure to simulations. The questionnaire identified issues
such as disorientation, oculomotor effects, and nausea and the
total score at the end reflected the severity of simulator
sickness experienced by the users. SSQ is widely applied in
the research studying the propensity of inducing
cybersickness and simulator sickness.
Although SSQ is the primary measure of cybersickness
symptoms [11], it was never intended to be used in the context
of virtual reality. And to make matters even worse, it was
devised 25 years ago. As a result of the above mentioned
issues, SSQ has received quite a bit of criticism over the years.
There have been many attempts during the past to revise and
refactor the SSQ.
Figure 1. Symptoms in MSQ & SSQ [30]
Kim et al. (2004) [32] came up with a revised version of
the SSQ claiming that the original SSQ was missing several
key symptoms and lacked interpretability. The new scale was
named Revised-SSQ (RSSQ) by the team and consisted
significant changes to the measuring criteria and the scale.
They ditched the original 4 option ordinal response scale and
opted for an 11 option scale with two labels at the two
extremes. Despite the efforts RSSQ didn’t replace the original
SSQ mainly because of its intimidating scale and the under-
representative study group.
Bouchard et al. (2007) [33] attempted to refactor the SSQ
with the intentions of making it specific to cybersickness.
They were convinced that the SSQ had multiple subfactors
which resulted in a biased total score and expressed their
concerns regarding the use of military pilots in the SSQ
development study arguing the applicability to general adult
population. To address these problems, they used a non-
military study group to avoid cross-loading and devised a new
questionnaire with two main categories: nausea and
oculomotor. Although a higher portion of females were used
in the study, many researchers argued that the study sample
was still unrepresentative. As a result of the above mentioned
concern, the usage of this refactored version has been limited
Bruck and Watters (2011) [34] tried to incorporate
physiological responses to the factor structure of the SSQ by
using respiration and heart rate as variables alongside the
original 16 symptoms of the SSQ. The sample size used in the
study was better in terms of gender representation but had
received some negative remarks due to its limited size. And
the use of a principal components analysis (PCA) rather than
a common factor model has been criticised by some
researchers [11]. Same as the other refactoring attempts,
there’s no evidence to prove that this version is significantly
better than the original.
Ames et al. (2005) [35] designed a refactored version of
the SSQ called Virtual Reality Symptom Questionnaire
(VRSQ) to be distinctively used in the context of virtual
reality. A seven option response scale with four descriptive
labels was utilized in the VRSQ and unlike SSQ, VRSQ was
intended to be specifically used for the measure of
cybersickness. However, VRSQ has not been validated in the
context of virtual reality and the methodology followed during
the study is questionable mainly due to the use of a non-
interactive video feed on a HMD [11]. Hence the measure has
received very little attention.
Stone III (2017) [11] did a psychometric evaluation of the
SSQ and pointed out the need for an empirical measure for
cybersickness. He further argued that the use of the SSQ in the
context of virtual reality to be problematic and stated that the
refactored versions failed to provide an obvious advantage
over the original version. The study resulted in a new measure
called Cybersickness Questionnaire (CSQ), a two factor
exploratory model with a different factor structure and scoring
mechanism. What is noteworthy about this new measure is its
ability to be collaboratively used with the SSQ. According to
Stone III (2017) [11], researchers have the opportunity to
administer the original SSQ if they wish and use the CSQ’s
scoring criteria afterwards.
The use of single-item measures instead of the SSQ have
been a common practice lately since the longer questionnaires
usually take a lot of time to carry out requiring for frequent
breaks which might result in diminished cybersickness
symptoms [29]. Majority of these scales required the user to
rate their feelings of either nausea, motion sickness, or
discomfort throughout the course of the study at regular
intervals. Nalivaiko et al. (2015) [36] used a single-item, 11
option nausea rating scale in their study with labels “0-no
signs of nausea” and 10-ready to vomit” at corresponding
extremes. Rebenitsch and Owen (2014) [37] utilised an
immersion rating in their study and continuously inquired the
participants to rate the experience on a scale of 0 - 10. This
method ensured the safety of the participants and they
reportedly discontinued the exposure if anyone responded
“10”. Although these single-item scales are adequate to
monitor cybersickness symptoms, they are not as sensitive or
informative as the SSQ and its refactored versions [29].
B. Postural Instability based approaches
The core concept behind the postural instability theory is
that the maintenance of body posture is crucial and prolonged
instability of posture may lead to visually induced motion
sickness (VIMS) [19]. Some of the VIMS symptoms are
drowsiness, dizziness, fatigue, pallor, cold sweat, oculomotor
disturbances, nausea, and vomiting [38]. The aforementioned
symptoms are very similar to that of Vection and the
relationship between Vection and VIMS had been questioned
and debated over the years in the literature [39]. This approach
sounds convincing on paper since it doesn’t require to bother
the user during the simulation and most importantly due to its
objective nature. But in practice this approach seemed to
violate the above advantages simply because of the
standardized stance requirement [29].
Researchers at the University of Waterloo are
reportedly working on defining a predictive model to identify
the likelihood of cybersickness in individuals by analysing
their postural swaying patterns as a response to moving visual
field. According to the research team, knowing who might be
susceptible to cybersickness beforehand would help in
minimising the dawn of symptoms [40].
Dennison & Zmura (2017) [39] devised an experiment to
test out the link between postural sway and cybersickness.
They exposed a group of standing and seated HMD wearing
test subjects to a visual feed of a virtual tunnel that rotated
along their line of sight. It was discovered that only a minority
of the group exhibited postural sway in response to the
commencement of the tunnel rotation. The increase of the
rotational speed of the tunnel directly resulted in increase of
cybersickness. The duo claimed that the subjects who
exhibited greater levels of cybersickness, showed less
variations in postural sway.
C. Physiological/ Biometric approaches
Cybersickness symptom prediction using physiological
signals and biometrics have received a noticeable interest in
the past mainly due to the availability of affordable medical
technology. Several research groups have pointed out
prospective physiological consequences of cybersickness
from time to time [5], [41], [42]. And by following this
approach, researchers are able to gather evidence of symptoms
without intruding the user. Heart rate and blood pressure
measurements are commonly used for analysis [29] but some
research groups have managed to expand their horizons by
integrating different physiological signals such as brain
activity, skin reaction etc.
Kiryu et al. (2007) [43] used electrocardiogram (ECG),
blood pressure and respiration in their study to figure out the
trigger factors and accumulation factors in cybersickness. The
power frequencies of the physiological signals were used in
estimating the sensation intervals and the onset of
Kim et al. (2005) [42] examined 16 electrophysiological
signals of 61 test subjects over a period of 9.5 minutes. Three
susceptibility questionnaires were obtained pre-exposure and
the physiological signals were recorded before, during and
after the immersion. The SSQ was administered at the end of
the exposure for the test subjects to report their cybersickness
severity. They were able to figure out a strong positive
correlation between cybersickness and gastric
tachyarrhythmia, eye blinking rate, heart rate and EEG.
Dennison & Zmura (2017) [5] examined ECG,
electrogastrogram (EGG), electrooculogram (EOG),
photoplethysmogram (PPG), breathing rate and galvanic skin
response (GSR) in their study to figure out the feasibility of
using physiological signals to predict cybersickness. Apart
from the physiological measures, they administered the SSQ
and asked the users to verbally rate the cybersickness severity
every two minutes. The main purpose of using the SSQ in the
study was to figure out the possibility of predicting sickness
scores on the SSQ by using the physiological changes. And
they were able to find significant correlations in the SSQ
scores and physiological signals.
D. Predictive Model based approaches
Use of a predictive model to predict cybersickness seems
highly constructive but upon close examination of the
literature, it was found that only a handful have even
attempted the said approach in their studies.
Design Interactive is a tech company based in the United
States and they have been studying cybersickness for over two
decades. They have been able to develop a predictive
algorithm which uses both the questionnaire & physiological
approaches in unison [44]. Unfortunately, Design Interactive
only provides services to their exclusive clientele. Therefore
the algorithm is not publicly available.
Bockelman and Lingum (2013) [24] have pointed out the
need for a cybersickness prediction tool & indicated their
interest in working towards an open source tool to predict the
possibility of cybersickness. They further reported that they
have been working on a predictive model to provide guidance
to virtual reality users. But the success rate or the progress of
the research is unknown.
By conducting a comprehensive investigation, we were
able to distinguish the pros and cons of each study and most
importantly we were able to eliminate the unlikely factors that
could potentially cause cybersickness to avoid any over fitting
A. Analysis of the existing questionnaires
The original version of the SSQ exhibits substantial
differences in comparison to the revised and refactored
versions with regards to the context it was intended to be used,
number of items in the questionnaire and the factor model.
Table 1 depicts a comparison between the original SSQ and
its refactored versions.
Kennedy et al.
Kim et al.
Bouchard et al.
Bruck and
Watters (2011)
Ames et al.
of Items
* SS Simulator Sickness, CS - Cybersickness
Table 1. Comparison between the exisiting questionnaires
B. Analysis of the physilogical signals
Table 2 displays a brief analysis of some important
physiological measures and their correlation to cybersickness.
Physiological Signal
Correlation to
Blood Pressure
The pressure between
heart beats
Electrocardiogram (ECG)
Monitors the heart
Electroencephalogram (EEG)
Monitors electrical
activity due to current
flow in the brain
Electrogastrogram (EGG)
Detects stomach
movement and strength
Electrooculogram (EOG)
Detects the movement
of the eye
Galvanic Skin Response
Monitors changes in
electrical resistance of
the skin
Respiration (RSP)
Monitors inhalation of
Table 2. Physiological signals and their correlation to cybersickness
C. Analysis of the Individual Differences affecting
Table 3 depicts an analysis of the individual factors taken
from cybersickness specific researches.
Correlation to
Young subjects proved to be more
Females appeared to be more
Not tested for cybersickness
Repeated immersion decreases
cybersickness susceptibility
Concentration Level
Diminished concentration likely
reduces cybersickness
Postural Instability
Unstable posture leads to
History of Illnesses
(Headaches, Motion
Sickness, Migraines,
History of illness would likely
increase the likelihood of
BMI has proven to have a minor
effect on cybersickness
* BMI Body mass index
Table 3. Analysis of individual differences
D. Analysis of Software and Hardware factors
Table 4 presents a summary of the prospective software
and hardware factors which affects cybersickness.
Frame rate
Field of view (FOV)
Scene complexity
Head Tracking
Table 4. Summary of Hardware & Software factors affecting
The ultimate goal of this study was to review the current
status of cybersickness research with the intentions of deriving
a predictive algorithm for cybersickness susceptibility. During
the study we realised that the performance of the already
existing approaches on their own were subpar and the use of a
hybrid approach seemed to be a highly versatile option.
In many cases, individual differences and software &
hardware factors had not been taken in to consideration. Some
of these factors have proven to be highly correlated with
cybersickness, hence ditching these would not be a smart idea.
Any new approach should take leverage of the existing work
and should carefully consider all the possible contributing
factors. On the other hand, machine learning and deep learning
is on the rise and the use of the said technologies would
enhance the results of the solution significantly.
Overall, Regardless of the realism or interactive abilities,
current VAMR systems are not usable for a prolonged period
of time and they are a risk to the users because of
The primary focus has been given to finding the
underlying reasons of cybersickness and VAMR’s
predisposition of inducing cybersickness but cybersickness
prediction has received very little to no attention. However,
upon close inspection of literature, it has been identified that
only subjective analysis is often used in cybersickness studies
and the current primary measure of cybersickness (SSQ) is
massively outdated. Whereas, other than subjective factors,
there are a numbers of possible contributing factors impacting
cybersickness which are often overlooked.
While exploring the literature, we strongly felt the need for
an all-round predictive model and through the analysis of the
existing work, we were able to identify the key factors and
parameters necessary for deriving such an algorithm. With the
gathered knowledge, we hope work towards a predictive
algorithm which can be used by the application developers to
check their products for cybersickness susceptibility prior to
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... Many of these factors have been investigated in research papers, often by using various questionnaires for obtaining subjective metrics, and equipment for assessing a participant's objective measures such as heart rate, electrocardiogram, and skin conductance devices (Baumgartner et al., 2006;Dennison et al., 2016;Egan et al., 2016;Keshavarz et al., 2015a). A review paper by Brion and Pumudu (2018) provides a chronicle of research papers which use subjective and/or objective metrics to evaluate and create some form of prediction of cybersickness (Brion & Pumudu, 2018). However, few papers have explicitly addressed the impact of navigation speed on cybersickness and stress level in VR (Medeiros et al., 2016;So et al., 2001). ...
... Many of these factors have been investigated in research papers, often by using various questionnaires for obtaining subjective metrics, and equipment for assessing a participant's objective measures such as heart rate, electrocardiogram, and skin conductance devices (Baumgartner et al., 2006;Dennison et al., 2016;Egan et al., 2016;Keshavarz et al., 2015a). A review paper by Brion and Pumudu (2018) provides a chronicle of research papers which use subjective and/or objective metrics to evaluate and create some form of prediction of cybersickness (Brion & Pumudu, 2018). However, few papers have explicitly addressed the impact of navigation speed on cybersickness and stress level in VR (Medeiros et al., 2016;So et al., 2001). ...
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The aim of this research was to determine if the speed of movement in virtual environments has an impact on cybersickness as potentially experienced by an end user of the system. Cybersickness is a common side effect in virtual reality (VR) systems, shown to have a negative influence on user experience. It can be described as a mismatch between vestibular and oculomotor sensors, where a person has a feeling of movement even though there is none. In this research, we study the impact of different navigation speeds in VR on cybersickness, relying on both subjective ratings indicating cybersickness symptoms, and objective measures of stress level. We wanted to find out if there is a correlation between objective and subjective metrics used. For test purposes, we used the HTC Vive VR headset and scenery from the Talos Principle VR game (where subjects can easily shift between three different movement speeds). Subjective ratings were collected using a questionnaire involving 11 questions used to evaluate cybersickness symptoms. Objective metrics were collected using the Pip Biosensor. A total of 28 participants took part in the study, while 2 participants withdrew due to physical discomfort. Results obtained from the Pip device show no statistically significant difference between navigation speeds for relaxed, stressed, and steady states. Some statistically significant correlations were found between gender and stomach ache, need to vomit, and physical discomfort while wearing HMD. Furthermore, correlation was found between age and variables of nausea in transport vehicles and vertigo. Other correlations found are described in the results section of the paper.
... Human factors Various sources (e.g., [11,15,46,95,[112][113][114]) list some of the individual factors that may be linked to a greater susceptibility to cybersickness, such as: ...
User acceptance of virtual reality (VR) applications is dependent on multiple aspects, such as usability, enjoyment, and cybersickness. To fully realize the disruptive potential of VR technology in light of recent technological advancements (e.g., advanced headsets, immersive graphics), gaining a deeper understanding of underlying factors and dimensions impacting and contributing to the overall end-user experience is of great benefit to hardware manufacturers, software and content developers, and service providers. To provide insight into user behaviour and preferences, researchers conduct user studies exploring the influence of various user-, system-, and context-related factors on the overall Quality of Experience (QoE) and its dimensions. When planning and executing such studies, researchers are faced with numerous methodological challenges related to study design aspects, such as specification of dependant and independent variables, subjective and objective assessment methods, preparation of test materials, test environment, and participant recruitment. Approaching these challenges from a multidisciplinary perspective, this paper reviews different aspects of performing perception-based QoE assessment for interactive VR applications and presents options and recommendations for research methodology design. We provide an overview of different influence factors and dimensions that may affect the overall QoE, with a focus on presence, immersion, and discomfort. Furthermore, we address ethical and practical issues regarding participant choice and test material, present different assessment methods and measures commonly used in VR research, and discuss approaches to choosing study duration and location. Lastly, we provide a concise analysis of key challenges that need to be addressed in future studies centered around VR QoE.
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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 measures of sensorimotor processing. In a single session, we conducted a battery of tests of balance control, vection responses, and vestibular sensitivity to self-motion. Following this, we measured cybersickness after VR exposure. We constructed a principal components regression model using the measures of sensorimotor processing. The model significantly predicted 37% of the variability in cybersickness measures, with 16% of this variance being accounted for by a principal component that represented balance control measures. The strongest predictor was participants' sway path length during vection, which was inversely related to cybersickness (r(28) = -.53, p = .002) and uniquely accounted for 7.5% of the variance in cybersickness scores across participants. Vection strength reports and measures of vestibular sensitivity were not significant predictors of cybersickness. We discuss the possible role of sensory reweighting in cybersickness that is suggested by these results, and we identify other factors that may account for the remaining variance in cybersickness. The results reiterate that the relationship between balance control and cybersickness is anything but straightforward.
Conference Paper
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Augmented Reality is on the rise with consumer-grade smart glasses becoming available in recent years. Those interested in deploying these head-mounted displays need to understand better the effect technology has on the end user. One key aspect potentially hindering the use is motion sickness, a known problem inherited from virtual reality, which so far remains under-explored. In this paper we address this problem by conducting an experiment with 142 subjects in three different industries: aviation, medical, and space. We evaluate whether the Microsoft HoloLens, an augmented reality head-mounted display, causes simulator sickness and how different symptom groups contribute to it (nausea, oculomotor and disorientation). Our findings suggest that the Microsoft HoloLens causes across all participants only negligible symptoms of simulator sickness. Most consumers who use it will face no symptoms while only few experience minimal discomfort in the training environments we tested it in.
Conference Paper
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As virtual reality (VR) applications expand in private and public sector contexts, so do reports of sickness elicited within VR systems. Users of head mounted VR displays frequently report symptoms similar, but not identical, to those of motion sickness and simulator sickness. Because of this distinction, the symptoms are collectively classified as symptoms of cybersickness. While researchers and tech developers alike acknowledge VR’s propensity for inducing cybersickness, there is no symptom prediction tool. The present paper describes a research agenda which will culminate in a cybsersickness prediction tool. First, the authors clarify nomenclature relevant to the VR, virtual environments (VE), and cybersickness. The preliminary literature review resulted in a test Cybersickness Index Matrix (CIM), with three cybersickness trigger categories: System, Task, Individual Differences. Researchers conducted a validation test of the CIM in a pilot study conducted in conjunction with an energy industry training program. The paper presents those preliminary results and provides a discussion including CIM refinement and future implementation potential.
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Cybersickness is an affliction common to users of virtual environments. Similar in symptoms to motion sickness, cybersickness can result in nausea, headaches, and dizziness. With these systems becoming readily available to the general public, reports of cybersickness have increased and there is a growing concern about the safety of these systems. This review presents the current state of research methods, theories, and known aspects associated with cybersickness. Current measurements of incidence of cybersickness are questionnaires, postural sway, and physiological state. Varying effects due to display and rendering modes, such as visual display type and stereoscopic or monoscopic rendering, are compared. The known and suspected application aspects that induce cybersickness are discussed. There are numerous potential contributing application design aspects, many of which have had limited study, but field of view and navigation are strongly correlated with cybersickness. The effect of visual displays is not well understood, and application design may be of greater importance.
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Evidence from studies of provocative motion indicates that motion sickness is tightly linked to the disturbances of thermoregulation. The major aim of the current study was to determine whether provocative visual stimuli (immersion into the virtual reality simulating rides on a rollercoaster) affect skin temperature that reflects thermoregulatory cutaneous responses, and to test whether such stimuli alter cognitive functions. In 26 healthy young volunteers wearing head-mounted display (Oculus Rift), simulated rides consistently provoked vection and nausea, with a significant difference between the two versions of simulation software (Parrot Coaster and Helix). Basal finger temperature had bimodal distribution, with low-temperature group (n=8) having values of 23-29°C, and high-temperature group (n=18) having values of 32-36°C. Effects of cybersickness on finger temperature depended on the basal level of this variable: in subjects from former group it raised by 3-4°C, while in most subjects from the latter group it either did not change or transiently reduced by 1.5-2°C. There was no correlation between the magnitude of changes in the finger temperature and nausea score at the end of simulated ride. Provocative visual stimulation caused prolongation of simple reaction time by 20-50ms; this increase closely correlated with the subjective rating of nausea. Lastly, in subjects who experienced pronounced nausea, heart rate was elevated. We conclude that cybersickness is associated with changes in cutaneous thermoregulatory vascular tone; this further supports the idea of a tight link between motion sickness and thermoregulation. Cybersickness-induced prolongation of reaction time raises obvious concerns regarding the safety of this technology. Copyright © 2015. Published by Elsevier Inc.
It has been suggested that postural instability is necessary for cybersickness to occur. Seated and standing subjects used a head-mounted display to view a virtual tunnel that rotated about their line of sight. We found that the offset direction of perceived vertical settings matched the direction of the tunnel’s rotation, so replicating earlier findings. Increasing rotation speed caused cybersickness to in- crease, but had no significant impact on perceived vertical settings. Postural sway during rotation was similar to postural sway during rest. While a minority of subjects exhibited postural sway in response to the onset of tunnel rotation, the majority did not. Furthermore, cybersickness increased with rotation speed similarly for the seated and standing conditions. Finally, subjects with greater levels of cyber- sickness exhibited less variation in postural sway. These results lead us to conclude that the link between postural instability and cybersickness is a weak one in the present experiment.
Cybersickness is a common and unpleasant side effect of virtual reality immersion. We measured phys- iological changes that were experienced by seated subjects who interacted with a virtual environment (VE) first while viewing a display monitor and second while using a head-mounted display (HMD). Comparing results for these two conditions let us identify physiological consequences of HMD use. In both viewing conditions, subjects rated the severity of their symptoms verbally and completed a post- immersion cybersickness assessment questionnaire. In the HMD viewing condition but not in the display monitor condition, verbal reports of cybersickness severity increased significantly relative to baseline. Half of the subjects chose to exit the VE after six minutes of HMD use and reported feeling some nausea at that time. We found that changes in stomach activity, blinking, and breathing can be used to estimate post-immersion symptom scores, with R2 values reaching as high as 0.75. These results suggest that HMD use by seated subjects is strongly correlated with the development of cybersickness. Finally, a linear discriminant analysis shows that physiological measures alone can be used to classify subject data as belonging to the HMD or monitor viewing condition with an accuracy of 78%
We examined background characteristics of virtual reality participants in order to determine correlations to cybersickness. As 3D media and new VR display technologies from companies such as Occulus and Sony become more popular, the incidence of cybersickness is likely to increase. Understanding the impact of individual backgrounds on susceptibility can help shed light on which individuals are more likely to be impacted. Past history of motion sickness and video game play have the best predictive power of cybersickness of the factors studied. A model to estimate the likelihood of cybersickness using background characteristics is posed.
The widespread diffusion of immersive virtual environments (VE) is threatened by persistent reports that some users experience simulation sickness, a form of motion sickness that accompanies extended use of the medium. Experience with the problem of simulation sickness is most extensive in the military where the illness has accompanied the use of various simulators since the 1950s. This article considers the obstacles presented by simulation sickness to the diffusion of VE systems, its physiological and technological causes, and, finally, the remedies that have been suggested to fix the problems. This issue is also considered in light of previous reports of purported illnesses that accompanied the diffusion of other communication technologies.