Title: Frequency Characteristics of Visually Induced Motion Sickness
Author names and affiliations: Cyriel Diels, Peter A. Howarth
Coventry School of Art and Design, Coventry University, Priory Street, Coventry CV1 5FB,
Peter A. Howarth
Environmental Ergonomics Research Centre, Loughborough Design School, Loughborough
University, LE11 3TU, United Kingdom
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Word count reference: 1131
Objective: The aim of this study was to explore the frequency response of “Visually Induced
Motion Sickness” (VIMS) for oscillating linear motion in the fore-and-aft axis.
Background: Simulators, virtual environments, and commercially available video games that
create an illusion of self-motion are often reported to induce the symptoms seen in response
to true motion. Often this human response can be the limiting factor in the acceptability and
usability of such systems. Whereas motion sickness in physically moving environments is
known to peak at an oscillation frequency around 0.2 Hz, it has recently been suggested that
VIMS peaks at around 0.06 Hz following the proposal that the summed response of the visual
and vestibular self-motion systems is maximized at this frequency.
Methods: 24 participants were exposed to random dot optical flow patterns simulating
oscillating fore-and-aft motion within the frequency range of 0.025 – 1.6 Hz. Before and
after each 20 min exposure VIMS was assessed using the SSQ. Also, a standard motion
sickness scale was used to rate symptoms at one minute intervals during each trial.
Results: VIMS peaked between 0.2 and 0.4 Hz. with a reducing effect at lower and higher
Conclusion: The numerical prediction of the “crossover frequency” hypothesis, and the
design guidance curve previously proposed, cannot be accepted when the symptoms are
Application: Under conditions in which stationary observers are exposed to optical flow
which simulates oscillating fore-and-aft motion at frequencies around 0.2-0.4 Hz should be
Contact information for reprints: Coventry School of Art and Design
Coventry University, Priory Street, Coventry CV1 5FB, Email: Cyriel.Diels@coventry.ac.uk
Short title version: Frequency Characteristics of VIMS
Keywords: simulator sickness, frequency, fore-and-aft motion, stimulus parameters
Précis: Effect of oscillating optokinetic fore-and-aft motion on visually induced motion
Simulation and Virtual Reality (VR) technologies are increasingly used for research, training,
design, and entertainment (Stanney, 2002). The ability to immerse users in interactive
synthetic environments offers some distinct advantages in that it provides a controlled and
safe environment in which individuals can repeatedly be exposed to scenarios that in real life
are too costly, dangerous, or simply non-existent. The ultimate acceptability and usability of
these technologies is however seriously limited by the fact that they are often reported to
induce Visually Induced Motion Sickness (VIMS), which is characterised by signs and
symptoms such as nausea, headache, fatigue, and drowsiness (Bos, 2011a; Kennedy et al.,
1990; Lawson et al., 2002; Wilson, 1996). VIMS significantly interferes with the intended
goals for which these technologies are used. In the context of training, it may hinder the
learning process, prevent individuals from participating in the training, limit the length of
time for which training can occur, and may lead to negative transfer of training (see also
Kennedy et al., 1990). In the wider context of entertainment, VIMS has been reported not
only when head-mounted displays have been used, but also when computer games have been
played using stand-alone monitors, along with the widespread occurrence during some TV
programmes and cinema films (see Howarth, 2008). Thus, there is a strong practical
motivation to gain a better understanding of the underlying causes of VIMS.
VIMS is a form of motion sickness that may occur when stationary observers are exposed to
moving visual images. Provided certain conditions are met (see Dichgans and Brandt, 1978),
moving visual images can induce an illusory sensation of self-motion, known as ‘vection’
(Tschermak, 1931). When visual motion is unaccompanied by physical self-motion, the
discrepancy between the self-motion cues provided by the visual system (i.e. vection) and the
lack of consistent signals from the vestibular and somatosensory systems is thought to
underlie the generation of VIMS (Reason and Brand, 1975; Oman, 1982; Bles et al., 1998).
Motion environments, including simulators, virtual environments, and commercially
available video games that create an illusion of self-motion, are frequently reported to induce
VIMS (Lawson et al., 2002) and may result in participant drop-out rates as high as 50%
(Reed et al., 2007). In order to be able to predict the incidence and severity of VIMS, one first
needs to identify contributing factors. More specifically, considering VIMS to be visually
induced, a logical first step would be the identification of the visual stimulus characteristics
that are most conducive to VIMS. This approach has previously been shown to be fruitful
with regard to seasickness. The ‘Motion Sickness Dose Value’ for predicting seasickness
based on the vertical motion of vessels (BSI, 1987) has been shown to be in accordance with
conditions that cause sickness at sea and is therefore of practical value in minimising motion
sickness (Griffin, 1990). Ultimately, the development of a ‘Cyber Sickness Dose Value’ (So
et al., 2001) may also prove to be instrumental in minimising the occurrence of VIMS in
For true motion sickness, the important physical characteristic of the provocative motion is
predominantly the frequency, and to a lesser extent the acceleration or amplitude of the
motion (Griffin, 1990; Guignard and McCauley, 1990). In laboratory studies using both
linear and angular oscillation, motion sickness peaks at a frequency of approximately 0.2 Hz,
whereas motion at other frequencies produces little or no sickness (Bos and Bles, 1998;
Donohew and Griffin, 2004; Golding and Markey, 1996; Golding et al., 1997, 2001; Griffin,
1990; Guignard and McCauley, 1990; O’Hanlon and McCauley, 1974). This is consistent
with what is known about the provocative motion profiles of transport systems associated
with motion sickness including ships, trains, aircraft, cars, and camels (e.g. Guignard and
McCauley, 1990; Lawther and Griffin, 1988).
The dominant frequency of oscillation of the visual scene or image may also play an
important role in the generation of VIMS (Kennedy et al., 1996), and, like true motion
sickness, imposed visual motion at a frequency around 0.2 Hz has been suggested to be most
provocative (Hettinger et al., 1990). However, until recently (Diels and Howarth, 2006;
Golding et al., 2009), there has been no published data to substantiate this specific frequency
dependence of VIMS. Golding et al. (2009) showed that visual Off Vertical Axis Rotation
(OVAR) was significantly more provocative at 0.2 Hz than at lower or higher frequencies, as
also observed with real motion. Parker and co-workers (Duh et al., 2004; Parker et al., 2001),
on the other hand, hypothesised VIMS to peak at a much lower frequency. Support for their
hypothesis was provided in a study employing concurrent visual and vestibular stimulation
(Duh et al. 2004) in which they evaluated the frequency response of the visual component by
evaluating postural balance whilst a visual scene was oscillating. They concluded that
“simulator sickness may be most readily evoked by visual-vestibular conflicts at the ‘cross-
over frequency’ – the frequency at which the summed response from the visual and vestibular
self-motion systems is maximum”, which they stated to be around 0.06 Hz. However, there
exists no published data to substantiate their hypothesis for the situation which is found far
more often, in which stationary observers are exposed to moving images such as are
encountered in fixed-base simulators, as well as in the consumer context of cinema and
television. It is these circumstances which are relevant if one wishes to provide a “design
guidance curve that indicates the frequency range of simulated motion that is likely to evoke
simulator or virtual reality sickness” (Duh et al 2004).
We report here two experiments designed to explore the frequency dependence of VIMS.
Studies into VIMS tend to expose observers to visual rotation about a vertical axis (e.g.
Bubka and Bonato, 2003; Duh et al., 2004; Golding et al. 2009), but rotation has however
only a limited role in the normal locomotion of the human observer. The principal motion
components that occur during normal (simulated) locomotion of a person are generally
translations and, more specifically, are usually translation along the line of sight in the
forward direction. Accordingly, in this study stationary observers were exposed to random
dot radial optical flow patterns simulating oscillating linear motion in the fore-and-aft axis.
The starting point in the first experiment was Duh et al.’s hypothesis, and we investigated
VIMS in the lower frequency range: 0.025, 0.05, 0.1, and 0.2 Hz, around their hypothesised
maximum. Following the failure to obtain results consistent with this hypothesis, a second
experiment was then conducted to extend the frequency range to 1.6 Hz. For brevity, the
methods and results for experiment 1 and 2 are presented together.
Following its approval by the Loughborough University Ethical Advisory Committee, 24
participants gave their informed consent to participate in the study. The first experiment
included 12 participants (7 male and 5 females) with a mean (± SD) age of 29.8 (± 5.8) years.
In the second experiment, a further 12 individuals (5 female and 7 male) with a mean (± SD)
age of 24.6 (± 2.8) years participated. All participants had intact vestibular function, were not
receiving any medication, and had normal or corrected-to-normal vision. The mean Motion
Sickness Susceptibility Questionnaire (MSSQ) percentile score for the participants in both
experiments was 44%, indicating the sample to be slightly less susceptible to motion sickness
than the normal population (Golding, 1998).
Apparatus and stimuli
The experiments took place in a dark room, and each participant had their head stabilised by
means of a head/chin rest (figure 1). The images were viewed binocularly from a fixed
viewpoint at a distance of 90 cm from the screen. To occlude the edges of the screen and
other peripheral features, participants wore goggles, which limited the visual field to 65º (h) x
59º (v) of angle. Acoustic localisation cues were masked by pink noise (75 dB) transmitted to
earphones. In addition, auditory alerting bleeps (500, 750, and 1000 Hz at 100 dB) were
played at random intervals throughout the exposure duration. Communication with the
participants during exposure was via a microphone. To control for eye movements,
participants were instructed to fixate a red dot (0.57º of visual angle) projected at eye height
in the centre of the screen. By means of an infrared camera aimed at the participants’ face,
instruction compliance was monitored in real-time by the experimenter.
Figure 1: Experimental setup.
The stimuli were generated in real time with a frame rate of 60 Hz using Matlab (version 6.5)
running on a DELL GX computer fitted with a Matrox Millenium P750 graphics card
(64Mb). The images were backprojected onto a tangent screen (190 cm x 145 cm) with a
Hitachi CP-X958W/E projector (1024 x 768 pixels). The display consisted of 500 white dots
with a luminance of 10.82 cd/m2 randomly positioned on a black background of 0.35 cd/m2.
Dot velocity and size varied exponentially as a function of their simulated location in depth
(Andersen and Braunstein, 1985). Dot size at the eye ranged from 0.22º at the middle to 2.97º
at the periphery. For technical reasons, there were no dots at the very centre of the visual
scene, and as a consequence, there was a black disc subtending 8.75º of visual angle. All
participants were exposed to random dot optical flow patterns simulating oscillating linear
motion in the fore-and-aft axis. In experiment 1, participants were exposed to oscillating
linear motion at the frequencies of 0.025, 0.05, 0.1, and 0.2 Hz. In experiment 2, the
frequencies employed were 0.2, 0.4, 0.8, and 1.6 Hz. At each frequency, the stimuli oscillated
with a peak angular velocity of 34°/s which pertains to a perceived peak velocity of 0.97 m/s.
Since peak optical velocity was held constant in this study, displacement and acceleration
covaried with frequency. The appearance to the participant was similar to the opening
sequence of the TV programme “Star Trek”, or the early MS Windows “starfield”
screensaver, but with back-and-forth motion rather than forward motion alone.
Experimental design and procedure
Participants were exposed to each of the conditions for 20 mins, and trials were separated by
at least 24 hrs to limit any habituation to the stimulus (Hill and Howarth, 2000). To avoid
possible circadian rhythm effects, each trial took place at the same time of day. A repeated
measures design was used, and to minimise order effects the sequence in which the
conditions were presented was balanced using a Latin square design. Prior to the first session,
participants received written and verbal instructions. When they indicated that they fully
understood the task, the experiment commenced. They were instructed to focus on the central
fixation dot for the duration of the experiment.
Motion sickness symptoms were assessed using the Simulator Sickness Questionnaire
(SSQ) (Kennedy et al., 1993). Measures of interest were the change (post – pre exposure
score) in the SSQ total scores and the change in SSQ subscores (N, O, D). In addition,
participants rated the severity of their motion sickness every minute on the standard sickness
scale produced by Bagshaw and Stott (1985) (1 no symptoms; 2 mild symptoms, but no
nausea; 3 mild nausea; 4 moderate nausea). The experiment was stopped once malaise rating
4 was reached or after 20 mins, whichever was the sooner. Participants who reached a
malaise rating of 4, and stopped, before 20 mins were assigned continuation values of 4. All
the participants were initially symptom-free and the measures of interest were (i) the time for
participants to first report a sickness rating of 2 (S2), (ii) the time to first report a rating of 3
(S3), (iii) the maximum sickness rating, (iv) the sum of the sickness ratings over the 20 min
exposure duration (‘accumulated sickness rating’). If no symptoms were reported, an
accumulated sickness rating and symptom onset time of 21 were recorded.
The occurrence of vection was assessed post exposure by asking participants the following
question: “Whilst watching the moving images, did you get the feeling of motion? Did you
experience a compelling sensation of self-motion as though you were actually moving?”
Vection was defined as a compelling feeling of self-motion, such as “the feeling you get
when a train moves next to you and you mistake it for your own motion.” To ensure
participants differentiated between object- and self-motion, prior to the first session, they
were exposed to oscillating roll motion (0.125 Hz; peak-to-peak amplitude of 120°) until a
compelling sensation of self-motion was reported. This typically occurred after about 15
Data analysis was performed using the software package SPSS (version 13). The data were
analysed twice. The first analysis considered the effects of session order, and because none
were identified, the analyses were repeated assuming no session order effect existed. Since
the motion sickness scales were not at an interval level of measurement, the data collected by
using these scales were analysed using a non-parametric approach. . The symptom onset time
and accumulated sickness rating distributions were heavily negatively skewed due to the
large number of participants reached the 20 min maximum exposure without reporting any
symptoms. To minimise the number of ties, a similar approach to that previously performed
by Golding (2003) was adopted. This used the fact that different SSQ total severity scores
were observed between the four conditions in some participants, indicating certain conditions
to be more provocative to them than others. SSQ total severity scores for such participants
were then employed to break ties. If SSQ total severity scores at 20 min were the same for
different conditions, the results were accepted as tied. Because of the abnormal distribution of
the data, differences between conditions were tested for significance using non-parametric
Wilcoxon Signed Ranks tests.
In experiment 1, 11 out of 12 participants experienced vection in the direction opposite that
of the display motion in all four conditions. One participant did not experience any vection
during 0.025 Hz oscillation but did so during oscillation at the other frequencies. In the
second experiment, three participants did not report any vection during 0.8 Hz oscillation
whereas one participant did not report vection during 1.6 Hz oscillation.
Table 1 shows the number of participants reaching each sickness rating stage before the 20-
min cut-off. It can be seen that in experiment 1 an increase in frequency produced greater
motion sickness. None of the participants reported nausea (sickness rating 3) during 0.025
and 0.05 Hz oscillation. During 0.2 Hz oscillation, however, two participants asked to
terminate the experiment before the maximum 20 min time cut off (at minute 17 and 18),
having reached sickness rating 4. The results of experiment 2 show the reverse in that an
increase in frequency beyond 0.2Hz resulted in reduced motion sickness. Two participants
had to terminate the experiment during 0.2 Hz oscillation after 6 and 8 min; one of these
participants also requested to stop the experiment during 0.4 Hz oscillation after 6 min.
Table 1. Number of participants reaching each sickness rating stage before the 20 min cut-off
for each frequency in Experiments 1 and 2
Experiment 1 Experiment 2
Sickness rating 0.025 0.05 0.1 0.2 0.2 0.4 0.8 1.6
1. No symptoms 12/12 12/12 12/12 12/12 12/12 12/12 12/12 12/12
2. Mild symptoms but no nausea 5/12 5/12 7/12 8/12 10/12 9/12 8/12 6/12
3. Mild nausea 0/12 0/12 2/12 3/12 2/12 4/12 2/12 1/12
4. Moderate nausea 0/12 0/12 0/12 2/12 2/12 1/12 0/12 0/12
Accumulated sickness rating
The mean accumulated sickness ratings for each frequency are shown in figure 2a. In
experiment 1, an increase in accumulated sickness rating was observed with increasing
frequency. The accumulated rating during 0.2Hz oscillation was significantly higher than
during 0.05 Hz oscillation (Z = 2.524, p = 0.012) and 0.025 Hz oscillation (Z = 2.240, p =
0.025). The rating during 0.1 Hz oscillation was significantly higher than that of the 0.025 Hz
oscillation (Z = 2.384, p = 0.017). The other differences seen were not statistically
significant. Beyond 0.2 Hz as evaluated in experiment 2, however, participants reported
lower sickness ratings with increasing frequency. Post-hoc comparisons revealed that the
accumulated sickness rating during 0.2 Hz oscillation was significantly higher than during 1.6
Hz oscillation (Z = -2.158, p = 0.031).
Figure 2. (a) Mean (± SEM) accumulated sickness rating and (b) mean (± SEM) time to
sickness rating 2 (O2) and 3 (O3) as a function of frequency for experiment 1 and 2.
Symptom onset time
Figure 2b shows the mean times to achieve sickness ratings 2 (mild symptoms, but no
nausea) and 3 (mild nausea). Since both measures failed to pass the tests for normality, non-
parametric statistics were used. In experiment 1, the time to achieve sickness ratings 2 and 3
both became shorter with higher frequencies. Post-hoc analysis showed that time to sickness
rating 2 during 0.2 Hz oscillation was significantly shorter than during either 0.05 Hz
oscillation (Z = -2.449, p = 0.014) or 0.025 Hz oscillation (Z = -2.668, p = 0.008). Time to
sickness rating 2 was significantly shorter during 0.1 Hz oscillation compared with oscillation
at 0.025 Hz (Z = -2.670, p = 0.008). Time to sickness rating 3 during 0.1 Hz oscillation was
significantly shorter than during 0.025 Hz oscillation (Z = -2.124, p = 0.034). No other
differences were found to be significant. As for the accumulated sickness ratings, in
experiment 2 the same effect was observed whereby time to achieve sickness rating 2 was
shortest during 0.2 Hz oscillation and became consistently longer with increasing frequencies
above this frequency. Time to achieve sickness rating 3 was shortest during 0.4 Hz oscillation
and became longer with frequencies both below and above 0.4 Hz. Due to the abnormal
distribution of both time to sickness rating 2 and 3, non-parametric tests were employed.
Post-hoc comparison showed that time to sickness rating 2 during 1.6 Hz oscillation was
significantly longer than during 0.4 Hz oscillation (Z = 2.123, p = 0.031). No other
differences were found to be significant.
Simulator Sickness Questionnaire (SSQ)
Table 2 shows the mean (SEM) SSQ total scores and the SSQ N, O, D subscores for each
frequency for experiment 1 and 2. In line with the other metrics in experiment 1, SSQ total
scores and subscores consistently increased with increasing frequency with the highest SSQ
scores observed during 0.2 Hz oscillation. Post-hoc analysis showed that the SSQ total score
and N subscore were significantly higher during 0.1 Hz than during 0.025 Hz oscillation (Z =
2.173, p = 0.030; Z = 2.692, p = 0.007, respectively). No other differences were found to
have reached statistical significance. In experiment 2, the SSQ total scores showed a steady
decrease with increasing frequency. However, no clear trend was observed in the SSQ
subscores. Post-hoc comparisons revealed no differences to have reached statistical
Table 2. Mean (SEM) SSQ total scores and N, O, D subscores for each frequency
Experiment 1 Experiment 2
Sickness rating 0.025 0.05 0.1 0.2 0.2 0.4 0.8 1.6
Total 19.0(5.0) 25.6(7.9) 35.5(10.9) 36.8(12.8) 17.3(5.4) 15.0(3.1) 14.9(3.7) 14.6(3.1)
N 12.7(3.9) 19.9(8.3) 31.0(8.9) 33.4(13.5) 13.9(6.9) 14.7(5.2) 11.3(3.8) 6.1(1.9)
O 19.0(5.3) 25.3(7.1) 28.4(9.2) 27.8(9.2) 17.2(4.7) 13.8(1.7) 16.5(4.1) 19.3(3.4)
D 17.4(5.5) 19.7(6.3) 34.8(13.7) 37.1(13.7) 12.7(5.5) 8.9(4.3) 8.9(3.9) 10.1(5.3)
This study was conducted to explore the frequency dependence of VIMS for linear oscillatory
motion in the fore-and-aft axis, and within the limits of our testing, 0.025 to 1.6 Hz, the level
of motion sickness was maximal within the frequency range of 0.2 - 0.4 Hz. Although the
SSQ total scores, accumulated sickness rating and time to sickness rating 2 all indicated
motion sickness to peak at 0.2 Hz, time to sickness rating 3 indicated 0.4 Hz oscillation to be
most provocative (see figure 2). The highest number of participants reaching sickness rating 2
was at a frequency of 0.2 Hz, but the highest number of participants reaching sickness rating
3 was at a frequency of 0.4 Hz.
The frequency of maximum nauseogenicity would appear, from our data, to lie between 0.2
Hz and 0.4 Hz. and it is clear that the results do not lend support to the hypothesis proposed
by Duh et al. (2004) according to which VIMS is expected to peak at a frequency of around
0.06 Hz. This is the value at which their visual and vestibular tuning functions cross, and
which they expect to have the maximum nauseogenicity. However, the ‘crossover frequency’
will change if these functions are not weighted equally, and our results would suggest that
they should not be.
The striking similarity in frequency-dependence between true motion sickness and VIMS
observed in the present study lends support for Hettinger et al.’s (1990) proposition that both
true and visual motion at a frequency around 0.2 Hz most readily evokes motion sickness. In
this context, it is worth examining how theories of motion sickness deal with its frequency
Benson (1988) proposed that during low frequency oscillation motion sickness occurs due to
a phase error in motion signals from the otoliths and somatosensory receptors. Von Gierke
and Parker (1994) further elaborated on this by suggesting a potential conflict not only
between the otoliths and somatosensory receptors but also the visceral graviceptors. Stott
(1986), on the other hand, suggested an intraotolith conflict at low frequency oscillations. The
central nervous system expects the otoliths overall output to average 1G over periods of time
greater than around 0.5 seconds. Unlike walking or running, which occur at higher
frequencies (> 1 Hz), this expectation is violated during sustained low frequency oscillations.
However, as there is no direct involvement of the vestibular system, other than it being silent,
neither of these hypotheses would appear to be able to explain the frequency response of
VIMS on the basis of the vestibular signals, apart from the fact that the expected signals are
An alternative explanation for the frequency tuning of motion sickness as well as its aetiology
is provided by the postural instability theory (Riccio and Stoffregen, 1991) according to
which motion sickness only occurs under conditions of prolonged postural instability. The
frequency dependence of motion sickness is explained by the overlap between imposed
stimulus motion and postural sway resulting in waveform interference which would be
greatest in the area of maximum overlap at around 0.2Hz (Stoffregen & Smart, 1998).
However, whereas several studies provide support for this theory, there are exceptions which
do not. These include observations of negative correlations between postural stability and
motion sickness as well as decreased stability over time accompanied by increases as
opposed to decreases in sickness (Bos, 2011b). As pointed out by Bos (2011b), postural
stability and motion sickness may be related via a common mechanism, but this does not
Currently, the most promising theoretical framework to explain the frequency dependence of
motion sickness appears to be the subjective vertical conflict model (Bles et al., 1998, 2008).
Within the subjective vertical conflict model, relevant visual and vestibular sensory signals
pass through a low pass filter with a Time Constant of 5s (=0.2Hz). At the same time, the
equivalent ‘efference copy’ signals (so-called ‘Internal Model’) pass through a filter with the
same frequency characteristics, before matching with the processed sensory signals in a
comparator. Because of filter characteristics a significant mismatch is detected by the
comparator at 0.2Hz and an output is given which initiates motion sickness (Bos et al., 2008).
At frequencies both below and above 0.2Hz, the degree of mismatch reduces as ultimately
reflected in lower motion sickness levels.
One limitation of the current experiments was that velocity was held constant across
frequencies, and thus, acceleration and displacement covaried with frequency. Although an
effect of displacement and acceleration on motion sickness cannot be ruled out, the consistent
frequency effect found with both constant (Duh et al., 2004) and varying (Lin et al., 2005)
peak velocity during rotational motion, suggests the frequency dependence of VIMS to be
largely independent of displacement and acceleration. Furthermore, if motion sickness was
dependent solely upon the peak velocity of the stimulus, the graph relating motion sickness to
frequency would have a gradient of zero. Alternatively, if motion sickness were governed
simply by acceleration, motion sickness and frequency would have shown a monotonic
relationship. This was clearly not the case, and it appears that, as for true motion sickness, the
principal physical characteristics of provocative motion include the frequency (or spectrum in
the case of complex motions) and to a lesser extent, the intensity (i.e., acceleration,
amplitude) of the motion. Nevertheless, it is acknowledged that future research will benefit
from the independent manipulation of both frequency and intensity to further enhance our
understanding of visual stimulus characteristics and VIMS. Considerations should also be
given to the use of optical flow patterns that allow for distance perception containing familiar
objects as opposed to abstract dots. However, whereas the stimuli used in the present study
may be less powerful than more realistic stimuli, there is no reason to believe that the tuning
effect observed would be different.
In summary, it has been previously argued that designers need to know the frequency
response of the visual stimulus provided to viewers of displays which have the potential to
cause VIMS. In our experiment, which involved participants viewing a star-like pattern of
stars, the maximum level of VIMS was found in the 0.2 – 0.4 Hz. region, with higher and
lower frequencies proving less powerful in generating symptoms. Thus the numerical
prediction of the “crossover frequency” hypothesis, and the design guidance curve previously
proposed, cannot be accepted when the symptoms are purely visually-induced.
Visually Induced Motion Sickness peaks between 0.2 and 0.4 Hz with a reducing
effect at lower and higher frequencies
The numerical prediction of the “crossover frequency” hypothesis cannot be accepted
when the symptoms are purely visually-induced
Under conditions in which stationary observers are exposed to dynamic visual
displays, optical flow which simulates oscillating fore-and-aft motion in the frequency
range of 0.2-0.4 Hz should be avoided
Bagshaw, M. & Stott, J. R. R. (1985). The desensitisation of chronically motion sick aircrew
in the Royal Air Force. Aviation, Space and Environmental Medicine, 56, 1144-1151.
Benson, A. J. (1988). Motion Sickness. In J. Ernsting and P.King (Eds.), Aviation
Medicine (2 ed., pp. 318-338): Butterworths.
Bles, W., Bos, J. E., de Graaf, B., Groen, E. & Wertheim, A. H. (1998). Motion sickness:
only one provocative conflict? Brain Research Bulletin, 47(5), 481-487.
Bos, J. E. (2011a). Visual Image Safety. Displays, 32(4), 151-152.
Bos, J. E. (2011b). Nuancing the relationship between motion sickness and postural stability.
Displays, 32, 189-193.
Bos, J. E., & Bles, W. (1998). Modelling motion sickness and subjective vertical mismatch
detailed for vertical motions. Brain Research Bulletin, 47(5), 537-542.
Bos, J. E., Bles, W. & Groen, E.L. (2008). A theory on visually induced motion sickness.
Displays, 29, 47-57.
British Standards Institution (BSI) (1987). Measurement and evaluation of human exposure
to whole-body mechanical vibration and repeated shock: BS6841. British Standards
Bubka, A., & Bonato, F. (2003). Optokinetic drum tilt hastens the onset of vection-induced
motion sickness. Aviation, Space and Environmental Medicine., 74(4), 315-319.
Crowley J.S. (1987). Simulator sickness: a problem for Army aviation. Aviation, Space and
Environmental Medicine; 58: 355–357.
Dichgans, J. & Brandt, T. (1978). Visual-vestibular interaction: effects on self-motion
perception and postural control. In R. Held, H. W. Leibowitz and H. L. Teuber
(Eds.), Handbook of sensory physiology (pp. 755-804). Berlin, Heidelberg:
Diels, C. & Howarth, P. A. (2006). Frequency dependence of visually-induced motion
sickness in the fore-and-aft direction. Aviation, Space and Environmental Medicine,
Donohew, B. E., & Griffin, M. J. (2004). Motion sickness: effect of the frequency of lateral
oscillation. Aviation, Space and Environmental Medicine, 75(8), 649-656.
Duh, H. B., Parker, D. E., Philips, J. O. and Furness, T. A. (2004). "Conflicting" motion cues
to the visual and vestibular self-motion systems around 0.06 Hz evoke simulator
sickness. Human Factors, 46(1), 142-153.
Förstberg, J., Andersson, E., Ledin, T. (1998). Influence of different conditions for tilt
compensation on symptoms of motion sickness in tilting trains. Brain Research
Bulletin, 5, 525-535.
Golding J.F. & Markey H.M. (1996). Effect of frequency of horizontal linear oscillation on
motion sickness and somatogravic illusion. Aviation, Space and Environmental
Medicine 67: 121-126.
Golding J.F., Finch M.I. & Stott J.R.R. (1997). Frequency effect of 0.35-1.0 Hz. horizontal
translational oscillation on motion sickness and the somatogravic illusion. Aviation,
Space and Environmental Medicine 68: 396-402.
Golding, J. F. (1998). Motion sickness susceptibility questionnaire revised and its
relationship to other forms of sickness. Brain Research Bulletin, 47(5), 507-516.
Golding, J.F., Phil, D., Mueller, A.G. & Gresty M.A. (2001). A motion sickness maximum
around the 0.2 Hz. frequency range of horizontal translational oscillation. Aviation,
Space and Environmental Medicine 72: 188-192.
Golding, J. F., Bles, W., Bos, J. E., Haynes, T. & Gresty, M. A. (2003). Motion sickness and
tilts of the inertial force environment: active suspension systems vs. active passengers.
Aviation, Space and Environmental Medicine, 74(3), 220-227.
Golding, J. F., Arun, S., Wortley, E., Wotton-Hamrioui, K., Cousins, S. & Gresty, M. A.
(2009). Off-vertical axis rotation of the visual field and nauseogenicity. Aviation,
Space and Environmental Medicine, 80(6), 516-521.
Griffin, M. J. (1990). Handbook of Human Vibration: Academic Press Ltd., New York.
Guignard, J. C. & McCauley, M. E. (1990). The accelerative stimulus for motion sickness.
In G. H. Crampton (Ed.), Motion and space sickness. Boca Raton, FL: CRC Press.
Hettinger, L. J., Berbaum, K. S., Kennedy, R. S., Dunlap, W. P., & Nolan, D. N. (1990).
Vection and simulator sickness. Military Psychology, 2(3), 171-181.
Hettinger, L.J. (2002) Illusory self-motion in virtual environments. In: Stanney K.M. , ed.
Handbook of virtual environments: design, implementation, and applications.
Mahwah, NJ: Lawrence Erlbaum Associates : 471–91.
Hill, K. J. & Howarth, P. A. (2000). Habituation to the side effects of immersion in a virtual
environment, Displays, 21(1), 25-30.
Howarth, P.A. (2008). The adverse health and safety effects of viewing visual images
Displays, 29(2), 45-46.
Hu, S. & Stern, R.M. (1998). Optokinetic nystagmus correlates with severity of vection
induced motion sickness and gastric tachyarrhythmia, Aviation, Space and
Environmental Medicine . 69(12) (1998) 1162-1165.
Kennedy, R. S., Hettinger, L. J. & Lilienthal, M. G. (1990). Simulator sickness. In G. H.
Crampton (Ed.), Motion and Space Sickness (pp. 317-341): Boca Raton, FL:CRC
Kennedy, R. S., Lane, N., Berbaum, K. S. & Lilienthal, M. G. (1993). Simulator sickness
questionnaire: An enhanced method for quantifying simulator sickness. The
International Journal of Aviation Psychology, 3(3), 203-220.
Kennedy, R. S., Berbaum, K. S., Dunlap, W. P. & Hettinger, L. J. (1996). Developing
automated methods to quantify the visual stimulus for cybersickness. Proceedings of
the 40th Human Factors and Ergonomics Society Annual Meeting. 1126-1130
Lawson, B., Graeber, D., Mead A. & Muth E. (2002). Signs and symptoms of human
syndromes associated with synthetic experiences. In: Stanney K.M., ed. Handbook of
virtual environments: design, implementation, and applications. Mahwah, NJ:
Lawrence Erlbaum Associates; 2002:589–618.
Lawther, A. & Griffin, M. J. (1988). Motion sickness and motion characteristics of vessels at
sea. Ergonomics, 31(10), 1373-1394.
Lin, J.J.W., Razzaque, S. and Parker, D.E. (2005). Effects of Simulated Motion Frequency in
Virtual Environments. Presented at the International Symposium on Theoretical
Issues in Ergonomics Science, July 18-21, 2005, San Diego, CA, USA.
Mach E. (1875) Grundlinien der Lehre von den Bewegungsempfindungen [Fundamentals of
the theory of movement perception]. Leipzig: Engelmann; 1875.
O'Hanlon, J. F., & McCauley, M. E. (1974). Motion Sickness Incidence As a Function of the
Frequency and Acceleration of Vertical Sinusoidal Motion. Aerospace Medicine, 5(4),
Oman, C.M. (1991) Sensory conflict in motion sickness: an observer theory approach. In:
Ellis S., Kaiser M. & Grunwals A., (Eds.) Pictorial communication in virtual and real
environments. London: Taylor and Francis; 1991:363–76.
Parker, D.E., Duh, B.L., Phillips, J.O. and Furness, T.A.III (2001). Self-motion system
frequency response: Implications for cybersickness. In Proceedings of Second
Biennial Space Biomedical Investigators, pp. 242-3.
Reason, J.T. & Brand, J.J. (1975). Motion sickness. London, UK: Academic Press.
Reed, N., Diels, C. & Parkes, A. M. (2007). Simulator Sickness Management: Enhanced
Familiarisation and Screening Processes. Proceedings of the First International
Symposium on Visually Induced Motion Sickness, Fatigue, and Photosensitive
Epileptic Seizures (VIMS2007). Hong Kong. Pp 156-162
Riccio, G. E., & Stoffregen, T. A. (1991). An ecological theory of motion sickness and
postural instability. Ecological Psychology, 3, 195-240.
So, R. H. Y., Ho, A. T. & Lo, W. T. (2001). A metric to quantify virtual scene movement
for the study of cybersickness: Definition, implementation, and verification. Presence,
Stanney, K. M. (Ed.) (2002). Handbook of Virtual Environments: Design, Implementation,
and Applications. Mahwah, New Jersey, London: Lawrence Erlbaum Associates.
Stoffregen, T. A., & Smart, L. J. (1998). Postural instability precedes motion sickness. Brain
Research Bulletin, 47, 437-448.
Stott, J. R. R. (1986). Mechanisms and Treatment of Motion Illness. In C. J. Davis, G. V.
Lake-Bakaar and D. G. Grahame-Smith (Eds.), Nausea and Vomiting: Mechanisms
and Treatment (1 ed., pp. 110-129). Berlin: Springer-Verlag.
Tschermak, A. (1931). Optischer raumsinn [Optical spatial awareness]. In A. Bethe, G.
Bergmann, G. Emden and A. Ellinger (Eds.), Handbuch der normalen und
pathologischen physiologie [Handbook of normal and pathological physiology].
von Gierke, H. E. & Parker, D. E. (1994). Differences in otolith and abdominal viscera
graviceptor dynamics: implications for motion sickness and perceived body position.
Aviation, Space and Environmental Medicine, 65(8), 747-751.
Wilson, J. R. (1996). Effects of participating in virtual environments: a review of current
knowledge. Safety Science, 23(1), 39-51.
Cyriel Diels UPDATE
Following his degree in Psychonomics at Utrecht University, Cyriel Diels worked in the
Visual Ergonomics Research Group at Loughborough University and the Applied Physics
Department at Waseda University, Tokyo, where he studied the relationship between visual
stimulus characteristics and visually induced motion sickness. Since being awarded his PhD
in 2003, he worked in the Human Factors and Simulation group at the Transport Research
Laboratory. In his current position at Jaguar Land Rover research his work focuses on HMI,
driver behaviour and performance, and simulation technology.
Peter A. Howarth
Peter A. Howarth started his working life as an Optometrist (an Ophthalmic Optician) before
returning to academia. His Masters in Ergonomics, from Loughborough University, was
followed by a spell on the West Coast of the USA when he worked in the Lawrence Berkeley
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Laboratory at the University of California at Berkeley. During this period he investigated the
Human Factors issue of how the human pupil responds to flicker. A year after he was
awarded his PhD from University of California at Berkeley in Physiological Optics, he
returned to England and took up his present position in what is now the Environmental
Ergonomics Research Centre, Loughborough Design School, Loughborough University.