1 Research

# Dynamic Field of View Reduction Related to Subjective Sickness Measures in
an HMD-based Data Analysis Task

Daniel Zielasko e-mail: {zielasko, freitag, weyers<EMAIL_ADDRESS>Alexander Meißner e-mail<EMAIL_ADDRESS>Sebastian
Freitag11footnotemark: 1 Benjamin Weyers11footnotemark: 1 Torsten W.
Kuhlen11footnotemark: 1 Visual Computing Institute, RWTH Aachen University,
Germany
JARA-HPC, Aachen, Germany

###### Abstract

Various factors influence the degree of cybersickness a user can suffer in an
immersive virtual environment, some of which can be controlled without
adapting the virtual environment itself. When using HMDs, one example is the
size of the field of view. However, the degree to which factors like this can
be manipulated without affecting the user negatively in other ways is limited.
Another prominent characteristic of cybersickness is that it affects
individuals very differently. Therefore, to account for both the possible
disruptive nature of alleviating factors and the high interpersonal variance,
a promising approach may be to intervene only in cases where users experience
discomfort symptoms, and only as much as necessary. Thus, we conducted a first
experiment, where the field of view was decreased when people feel
uncomfortable, to evaluate the possible positive impact on sickness and
negative influence on presence. While we found no significant evidence for any
of these possible effects, interesting further results and observations were
made.

Human-centered conceptsHuman computer interaction (HCI)Interaction
paradigmsVirtual reality; Human-centered conceptsHuman computer interaction
(HCI)VisualizationEmpirical studies in visualization

Introduction

Today, virtual reality (VR) increasingly integrates into our lives, be it for
entertainment or in professional contexts [29, 27, 22]. This may result in
people being exposed for long periods to immersive virtual environments (IVEs)
in an uncontrolled way. While longer exposure times have not yet been
sufficiently studied (one of the rare examples is [24]) it is already well
known that the symptoms of people that suffer from cybersickness [15] increase
over time. Broadly accepted as the main reason for cybersickness are sensory
conflicts [20], which can result from being immersed into the IVE while
simultaneously being exposed to potentially deviating real world physical
behaviors. Typical symptoms are nausea, dizziness and headache, which can last
several hours or even days [23] and thus can have a significant impact on the
fun and/or the productivity, next to the pure impact on the well-being of the
user. The group of people that are negatively affected is substantial and
actually may increase with the time of usage [23]. Nevertheless, some of the
factors that influence the degree of sickness a user suffers from can be
controlled. When using HMDs, one example is the size of the field of view
(FoV) [6, 16, 7]. However, the degree in which it can be manipulated, such
that they do not have other negative effects, is limited too. While these
negative effects, such as a reduced presence [21, 4, 16], might be acceptable
or even unrecognizable for someone who otherwise would get sick, it is
restricting for someone who does not exhibit any symptoms. Even though it is
possible to predict the vulnerability of a person from some individual factors
a priori, like gender, fitness, prior experience etc. [12], the variance is
big and it might additionally change even for a single person. Therefore,
Zielasko et al. [27] introduced the concept to utilize user profiles to manage
cybersickness countermeasures. For every user, a profile is created, initially
based on a set of demographic questions and experiences. Those variables
together with the users’ reactions to the experience then influence possible
sickness countermeasures, such as a decrease of the FoV. In the best case, the
initial predictions lose more and more influence while the system learns how
to deal with this individual user. But the described system today is a vision
and in practice this vision still offers a bunch of challenges that have to be
addressed. Thus, as the main contribution of this work we want to address one
of these challenges, namely if it is possible to reduce cybersickness by
radically reducing the FoV depending on the individual well-being without its
common drawbacks. Therefore, we designed and implemented a prototypical and
streamlined version of the proposed system. In a seated data analysis task
(the scenario matches the one defined by the term deskVR [29]), the FoV of the
study group’s participants adapted with respect to their repeatedly self-
reported well-being, while the control group experienced an unmodified FoV.

In the following section 1, related work regarding the avoidance and reduction
of cybersickness and the possibilities to measure cybersickness is brought
together. Then, the implementation of the FoV reduction, which was used in the
user study, is presented in section 2. Afterwards, the user study, the used
apparatus, task, procedure, the participants and finally our hypotheses are
presented in section 3. Its results are given in section 4 and discussed in
section 5. Finally, the work is concluded in section 6.

##  1 Sickness, Countermeasures and Measures

Cybersickness is influenced by many factors, some closer related to sensory
conflict theory [20] than others. These factors can be distinguished into two
groups: properties of the individual (that cannot be changed, but considered),
and system properties. An example of an individual factor is the user’s
experience with VR that is negatively correlated with cybersickness [2, 8, 3].
Here the user could be slightly introduced into the IVE by step-wise increased
exposure times [23], when circumstances permit. The factors given by the
system are potentially controllable and further split into three classes. The
first class consists of mandatory ones, like framerate, latency and jitter
[18, 25]. They are as fundamental and their impact on sickness is as high that
every VR setup should first aim for a sufficiently high framerate, a precise
tracking and low latency. Second, there are similar factors like utilizing
body cues for travel [28] that has shown to prevent sickness, but as they
usually do not come with disadvantages just want to be maximized. None of the
two factors really can be utilized as a dynamic cybersickness countermeasure
and worse even the combination of all factors might be not sufficient to
totally eliminate sickness. But there is a last class of factors, those which
it might be worth to vary as they have a negative impact as well. Only the
last are possible countermeasures for a profiled system. One example for this
is the FoV provided by the system. It is correlated to cybersickness and can
have positive effects when it is decreased [6, 16]. Additionally, the FoV can
be changed when using an HMD (see section 2). But different from the other
mentioned factors, it is usually the goal to maximize the FoV, which should
lead to higher immersion and thus presence [21, 4], which then again
correlates with sickness [26, 16, 17]. Thus, a general decrease of the FoV
might have either positive or negative effects on sickness but in any case
reduces immersion. Therefore, Fernandez and Feiner [7] dynamically and subtly
decreased the FoV with respect to the movement and rotation of the user such
that they should not notice it, leading to reduced negative effects. The
perception threshold is different from person to person, it is for instance
known that females tend to have a higher FoV [10], but this dynamic adaption
may be also integrated by default as it potentially serves no drawbacks.
Nevertheless, the change is by design just subtle and so it effects
potentially are subtle, as well. This raises the question whether the FoV can
be decreased much more radically, when the user starts to feel sick without
the same drastic negative effects, as the sickness might cover other side-
effects or the potential decrease in sickness compensates them. This would
raises one major need. How to continuously get a reliable insight to the
sickness level, respectively well-being, of the user, such that it is possible
to react to it? Research has found some correlations between biophysiological
measurements and cybersickness [14, 19, 5] that are comparatively easy to
obtain, but those are very sparsely examined. The work of Kim et al. [13]
serves as a first example for a system that actually changed the FoV and
warned the user to take a break in reaction to changes of biosignals. In
contrast, questionnaires like the simulator sickness questionnaire (SSQ) [11]
are well investigated and well established. However, it is not possible to
track the current well-being of a user with it and additionally the SSQ is
better suited for the comparison of populations than to judge the absolute
state of well being of a single individual, which is usually the case for
subjective measures like this. Thus, in this work we connected the size of the
FoV to the state of well-being of the participants by just regularly asking
for a rating on a simple scale [7, 9], knowing that this is only a compromise
in continuation as well as in reliability and additionally can disturb the
user and such has to be tackled in the future (see section 5).

##  2 Field of View Reduction

In the following, the technical implementation of the FoV reduction is
described. Different from the method used by Fernandez and Feiner [7], we do
not use a textured quad as a mask but a black full-screen quad. The cutout and
the blending into it are realized by a GLSL pixel shader that varies the
transparency values. Therefore, the cutout circle, given by the radius
$r_{inner}$, and the transition circle surrounding it, given by $r_{outer}$
(see Figure 1), are given by uniform variables with all position data given in
screen-space coordinates. This gives more control over the appearance of the
FoV limitation. The actual transparency $\alpha$ of a pixel $f$ is given by
its distance $d_{f}$ to the center as:

$\alpha(f)=\begin{cases}0,&\text{if }d_{f}<r_{inner}\\\ 1,&\text{if
}d_{f}>r_{outer}\\\
\frac{d_{f}-r_{inner}}{r_{outer}-r_{inner}},&\text{otherwise}\end{cases}$

This is mirrored for both eyes. The variables $r_{inner}$ and $r_{outer}$ are
varied to achieve the actual dynamic FoV reduction (see section 3.3). An
example of the FoV limitation is illustrated in Figure 1.

##  3 User Study

The study tasks were inspired by [28], where the participants had to use
different techniques to repeatedly travel through a large graph (5214 vertices
and 6913 edges) in node-link representation and find a highlighted pair of
vertices to identify the shortest path between (see section 3.2). In the study
that is described here the travel technique was kept constant and was a
standard 4 DoF gamepad control, i.e., roll rotation and vertical translation
were not enabled. However, the available FoV was varied between the
participants while in the study group there was an automatic field of view
reduction (see section 2 and 3.3) that was influenced by a personal health
state, which was updated through the subject during the experiment (see
section 3.2), in the control group, the health state was also updated by the
user but it induced no effects.

###  3.1 Apparatus

For all participants, the experiment took place in front of an regular office
desk, while they were seated on a tiltable office chair. The virtual
environment was projected by an Oculus Rift Consumer Version 1. The used
gamepad was an Logitech RumblePadTM 2\. Furthermore, all participants were
equipped with a chest strap to record the heart rate and two finger electrodes
to measure the skin resistance. These data is gathered as part of a long-term
experiment and thus is not evaluated here.

Figure 1:  A screenshot of the stereo image sent to the HMD: Depicted are the
graph and the slightly larger and red colored pair of spheres, which the
participant has to find the shortest path between. The right half is annotated
with indicators for the inner and outer radii of the FoV limitation.
Additionally, the screenshot is overlayed with a photo of the experimental
setup for the user study on the bottom.

###  3.2 Task

In the beginning of each task, the participant was instructed by a text in the
VE to find the shortest path between two highlighted vertices. To make the
task more difficult, further conditions were often added. For this purpose,
each edge was randomly colored, using a color out of a color blind safe set of
three colors (green, violet, and orange), using the same coloring scheme for
all participants. Two examples for additional conditions are, for instance,
find the shortest path with at least two orange edges or find the shortest
path without any violet edge. Every participant received the same tasks in the
same order, with generally increasing task difficulty. As soon as a
participant was convinced to have solved the task she triggered any button on
the gamepad to open a dialog. This dialog contained 10 spheres on a horizontal
line each labeled with a value, starting from one to ten. To answer the task a
participant had to look at the corresponding sphere, which changed its color
as feedback, and simultaneously trigger any button on the gamepad. We chose
this method over, e.g., a preselected sphere, where the selection is moved to
the right or the left with the sticks of the gamepad to reduce possible bias.
After answering, the participant was asked in a second dialog for her well-
being (in the following referred to as health score), again on a scale from 1
(good) to 10 (bad). Finally, the next task was specified in a dialog, which
had to be confirmed by pressing any button.

###  3.3 Field of View Settings

For the study group, the FoV was varied between $110^{\circ}$ (diagonal),
which is the maximum FoV of the used HMD, and $44^{\circ}$ at minimum.
Furthermore, every health score was linearly mapped to a specific FoV in this
range. When the FoV had to change this happened with a constant speed of
$0.44^{\circ}/s$. The FoV started at its maximum and did not change for first
two minutes of the experiment, i.e. for the whole training. In order to keep
the results clean, the FoV was only adjusted according to the health score,
i.e., the travel speed or rotation speed did not have any influence.

###  3.4 Procedure

Every participant of the experiment went to the following procedure. First,
everybody was asked to sign an informed consent form. Second, the participants
were non-anonymously asked, if they already had experienced VR. Based on their
answer and gender they were evenly distributed between the study condition and
the control condition, as several studies have shown a strong correlation
between those two factors and the sensitivity to cybersickness [12, 8, 2]. In
the following, a written description of the study tasks and procedure was
handed out with the request to be carefully read and the notice to ask the
experimenter as soon as any question arises. They were told that the
experiment is about spatial interaction in data analysis applications.
Additionally, the participants were asked to give preference to precision over
speed in the experiment. Afterwards, every participant anonymously filled out
a demographic questionnaire and an a priori simulator sickness questionnaire
(SSQ) [11]. Subsequent to all the paperwork, which took about 15 minutes, the
participants were equipped with the chest strap and electrodes (see section
3.1) and then seated in front of the desk. The participants were exposed
exactly 15 minutes to the virtual environment, of which the first 2 minutes
were reserved for practicing the controls and the procedure. The time was
restricted to keep the cybersickness scores comparable, while the participants
themselves were not aware that the study is terminated after 15 minutes. After
the experimental part of the study, everybody was asked to anonymously fill
out some concluding questionnaires including Likert-scale items, an a
posteriori SSQ and a Witmer and Singer presence questionnaire [26]. The whole
procedure took about 45 minutes per person. The textual content, both on paper
and in the IVE, was presented in both English and German side by side.

###  3.5 Participants

33 participants (8 female and 25 male, mean age 27.3, SD = 4.7) took part in
the experiment. Three of them (2 female, 1 male) canceled the study before its
end due to nausea. All three were part of the control condition, i.e., did not
receive any cybersickness countermeasures. The data of all three was excluded
from the analysis. It was possible to rebalance the conditions during the
study, which at the end still led to an even distribution regarding the number
of participants, gender and experience with VR. All participants were unpaid
and were only compensated with free candy and soft drinks.

###  3.6 Hypotheses

The experiment was mainly designed to, evaluate the general feasibility of
sickness counter-measures based on repeatedly measuring the subjective degree
of sickness and, second, to find out if a more radical decrease in the FoV
based on those measures has a negative impact on factors like presence. Thus,
the FoV of the study condition’s participants changed with respect to their
subjective well-being. Therefore, we expect the following effect.

  * H1

The participants in the study group experience less cybersickness then the
control group during and after the experiment.

Furthermore, we assumed that the reduction of the FoV will be noticed but
simultaneously does not hamper the participants as it reduces their sickness,
even though they might not be aware of this link. In summary, we did not
expect a negative effect on presence, as the FoV only changes for participants
who feel sick.

  * H2

The degree of presence experienced by the participants in the study group is
not different from the control group.

  * H2.1

The study group notices the field of view reduction.

  * H2.2

The study group does not feel disturbed by the field of view reduction.

##  4 Results

We report all results using a significance level of $.05$ and non-significant
trends at a level of $.1$. We analyzed the results with independent-samples
t-tests, using Welch-Satterthwaite adjustments to the degrees of freedom
instead where Levene’s test indicated that the assumption of homogeneity of
variances was violated. The results of the core parameters are given in Table
1 and the SSQ score is additionally depicted in Figure 2.

###  4.1 Core Measures

Regarding the core measures, the t-tests revealed no significant difference
between the study and the control group for the SSQ scores, the subjective
sickness measures, the presence score and the performance parameters, i.e.,
number of correctly solved tasks and the ratio of errors (see Table 1).
Additionally, some travel parameters were recorded and evaluated. Here we
found that the study group traveled a significantly longer distance
$T(28)=-2.180,p=.038$ and looked around to a higher degree,
$T(28)=-2.526,p=.017$. Last, the amount of virtual rotation, performed with
the gamepad, did not differ significantly.

Table 1: The results for the core measures for the study group (FoV) and the control group, in addition to, the p-values of the comparing t-tests. The $\Delta$SSQ score is the difference between an a priori SSQ and the a posteriori SSQ. The same holds for the $\Delta$health score. | Cond. | Mean | Std. Dev. | Sig.
---|---|---|---|---
SSQ score (a post.) |  | 47.6 | 38.5 |
| FoV | 31.7 | 29.3 | .212
$\Delta$ SSQ score |  | 35.7 | 35.2 |
| FoV | 20.7 | 24.9 | .190
last health score |  | 4.7 | 2.4 |
| FoV | 3.4 | 2.9 | .197
$\Delta$ health score |  | 2.9 | 1.9 |
| FoV | 2.3 | 2.8 | .542
presence |  | 95.8 | 17.0 |
| FoV | 98.0 | 28.8 | .813
# correct tasks |  | 10.7 | 4.7 |
| FoV | 11.7 | 2.7 | .483
task error in % |  | 22.3 | 23.4 |
| FoV | 17.0 | 8.1 | .417
distance traveled in $m$ |  | 1,343.4 | 462.1 |
| FoV | 1,772.8 | 606.8 | .038∗
head rotation in ∘ |  | 8,259.5 | 4,082.4 |
| FoV | 12,720.4 | 5,488.7 | .017∗
virtual rotation in ∘ |  | 6,583.6 | 3,789.4 |
| FoV | 8,885.6 | 5,304.6 | .182
Figure 2:  Box plots of the SSQ scores for the study and control condition.

Furthermore, the continuously gathered subjective health score for every user
was binned to a specific minute resulting in series of measurements (see
Figure 3), repeating the rating whenever there was no update in some minute.
This series was analyzed with a two-way mixed-design ANOVA with a between-
subjects factor of dynamic FoV reduction (enabled, disabled). However, there
was no statistical difference found between the two groups,
$F(12,336)=.846,p=.603)$.

Figure 3:  History of the health score (ranging from 1 (good) to 10 (bad))
updated by the participants during the experiments for the study group
(limited FoV) and the control group. The series is given starting with minute
3, excluding the training. Error bars show the 95% confidence intervals.

###  4.2 Subjective Measures

The results of the Likert-scale items are given in Figure 4. All but two
items, showed no statistically significant difference in the answers given by
the participants. First, for the control group the interaction in the IVE felt
significant more unnatural (Q6), $T(28)=2.947,p=.006$. Second, there was a
statistical trend indicating that the control group felt more exhausted
physically by the tasks (Q2), $T(28)=1.895,p=.068$.

Figure 4:  The answers to the Likert-scale items (1-5) for the study condition
(FoV) and the control condition. An asterisk marks a statistically significant
difference ($p<.05$) and a star in brackets an statistical trend ($p<.1$).

###  4.3 Correlation Analysis

A Pearson product-moment correlation coefficient was computed to assess the
relationship between the measures. We especially focused on possible
correlations between individual factors and sickness, as well as the behavior
of well-established measure such as the SSQ to the subjective health
questions. The tests found no correlation between the age, the experience with
VR or the subjective vulnerability to motion sickness to any cybersickness
measure. However, there was a strong positive correlation between the
subjective vulnerability to cybersickness and all sickness measures, e.g., for
the SSQ score, $r(30)=.496,p=.005$ and the last health question,
$r(30)=.539,p=.002$. Finally, there was a strong positive correlation between
all sickness measures, e.g., a posteriori SSQ score and last health questions,
$r(30)=.674,p<.001$.

##  5 Discussion

First to note is that we were not able to confirm H1, expecting that the study
group feels less sick then the control group after the experiments. None of
the sickness measures showed a significant difference. However, we are
confident that the reduction of the FoV had an effect, which is reflected in
the fact that the study group traveled a significantly longer distance and
looked around significantly more while simultaneously showing no significant
difference in the number of tasks correctly solved and errors made. Another
explanation would be that the partially reduced FoV decreased the grade of
presence as much that the positive effects on sickness, as discussed in
section 1, were counterbalanced [21, 4]. But neither the presence scores [26]
nor the Likert-scale item Q5 show a difference between the conditions and
their felt presence, which additionally confirms H2. Furthermore, the
participants of the study group seem to not have noticed the FoV reduction at
all, even though it was very drastic (down to about 48∘) for some
participants, as Q7 and Q8 suggest. Thus, H2.1 can surprisingly not be
confirmed and might suggest that the FoV may be reduced even more drastically,
faster or earlier when people feel sick. This outcome then confirms H2.2,
i.e., people were not disturbed by the restricted FoV. We think that a big
factor for the results is that the FoV was reduced with a delay, as the
participants first had to finish a task and in the following could report an
increased sickness. Additionally, the FoV started at its maximum and was not
reduced in the training. Even though we had a reason to do so, namely that the
participants should be able to freely explore the interfaces, an already
increasing discomfort was not counteract during this period. In summary, we
think that the results are promising, but that the FoV reduction was maybe
still to cautious and possible effects might be covered by the participants
that did not feel any sickness at all. A small indication for these effects
are suggested by the Likert-scale items Q2 and Q6. For the latter, the control
group rated the interaction significantly less natural. This might relate to
presence, but we are not completely sure how to interpret this result. For Q2
there was a statistical trend suggesting that the control group felt more
physically exhausted by the tasks. Thus, the experiment should be repeated
with more participants, a more drastic FoV reduction regarding amount, speed
and sensitivity and a longer exposure time, as the overall degree of sickness
was less than expected.

###  5.1 Health Score

The conditions from this experiment are not directly usable in practice. The
participants were repeatedly asked to rate their well-being during the study.
Although we found a strong correlation between the health score and the
established sickness measures, the possibility to repeatedly ask the user in a
standard application how they feel is usually not given, as it interrupts the
user. Furthermore, we are not sure if the self-perception is always reliable
and sufficiently fast. The reliability may be increased by asking the user
only if her well-being changed and if yes, in which direction. This may take
an unnecessary burden from the user and is as sufficient as an absolute rating
for the system, we think. However, as mentioned before, this might only be a
solution for further research but cannot be a final solution, as well as
giving the responsibility of updating the score to the user. Thus, for real
applications, biophysiological measurements might be an answer, but existing
research has to be extended [13, 14] and sensors have to be less cumbersome.
One impression we got during the experiments was that some people only started
to notice cybersickness effects in the moment they took off the HMD and the
minutes after. This might open up a new dimension as this cannot be countered
by the proposed method, at least not with subjective measures, but maybe with
biophysiological ones.

###  5.2 Individual Factors

In the presented experiment, there was no (initial) calibration or influence
based on demographic data. However, we analyzed the individual factors and
made some finds. In contrast to existing research [14], we found no
correlation between the subjective vulnerability to motion sickness and
measured cybersickness. However, we based this only on one simple question
asking if people suffer from motion sickness (given an explanation what this
is) with the possibility of answering, no, yes but rarely and yes, often. We
did the same for cybersickness with one additional option to answer: I never
used a VR system, and found a strong correlation to the sickness measures. Our
observation suggests two things: First, with the rise of consumer VR, people
often already had contact to VR hardware, at least within the age group we
studied and on a university campus, and second, they know whether they are
vulnerable to cybersickness or not. Finally, we found no correlation between
age and sickness, and no effect of VR experience and sickness, different to
existing research [8, 1], which both might be caused by the distribution of
the study participants.

###  5.3 Limitations

In addition to the limitations of the experiment that were already mentioned,
there are two more that serve as a foundation to future work. First, the FoV
reduction was performed evenly on the horizontal and on the vertical axis,
which might have different effects. Second, it should be more beneficial to
already start with a slightly restricted FoV, as it can quickly be opened up,
if the user feels good and has a faster effect if not.

##  6 Conclusion

In this work, we evaluated the impact of sickness induced field of view
reduction on cybersickness and presence measures. We found no significant
effects on cybersickness, nor an effect on presence. Nevertheless, the
experiment revealed some promising options for further investigations. The
most promising starting point seems to be finding a possibility to react
faster to upcoming sickness and searching for a more reliable tracking of
sickness. In any case, this topic gets more relevant with the increasing FoV
of consumer HMDs and the rising interest in the serious and productive
application of VR.

###### Acknowledgements.

The authors would like to acknowledge the support by the Excellence Initiative
of the German federal and state governments, the Jülich Aachen Research
Alliance – High-Performance Computing and the Helmholtz portfolio theme
“Supercomputing and Modeling for the Human Brain”. This project has received
funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 720270 (HBP SGA1).

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