The Impact of Psychological and Demographic Parameters on Simulator

Sickness

Stephan Schweig

, Magnus Liebherr

, Dieter Schramm

, Matthias Brand

and Niko Maas

University of Duisburg-Essen, Department of Mechatronics, Duisburg, Germany

University of Duisburg-Essen, Department of General Psychology: Cognition, Duisburg, Germany

Keywords:

Driving Simulator, Simulator Sickness, Aging, Mental Abilities, Mental Capacity, Adaption.

Abstract:

In a world, which is characterized by technical progress, virtual environment technologies become increasingly

relevant. In this context, simulators are used as a cost and time efﬁcient methods for investigating innovative

developments, training effects, but also the inﬂuence of individual attributes such as the process of aging.

Although the simulator technologies have been greatly developed in recent years, they are not able to perfectly

replicate the real world, which causes problems of system adaptation and simulator sickness. In the paper at

hand, we will focus both, the time of adaptation to the virtual environment and the phenomenon of simulator

sickness under the aspect of mental abilities and the process of aging, based on a driving simulator study with

414 participants.

1 INTRODUCTION

Within a previous study, Sahami, Jenkins, and Sayed

(Sahami et al., 2009) described the adaptation to a

driving simulator as the process by which drivers ad-

just their existing driving skills to the simulator so that

they can effectively control the simulated vehicle and

drive it through the simulated environment. A key

question in this context belongs to the time it takes

until humans adapt to virtual environments in order to

observe ”realistic” behavior. Previous studies mostly

used a pre-deﬁned period of time to practice from 5 to

15 min (Horberry et al., 2006) or in some case 30 min

to several hours (van Winsum et al., 1999). An excep-

tion is the study conducted by O’Neill, Krueger, Van

Hemel, McGowan, and Rogers (O’Neill et al., 1999),

in which the participants were asked to practice two

full days before starting the experiment. In contrast,

Bass, Charlton and Bastin (Baas et al., 2000) used a

very short practice time of around 2 min in examine

truck drivers fatigue and ﬁtness of duty. Along with

a pre-deﬁned period of time, pre-deﬁned distances

(Lewis-Evans and Charlton, 2006), or the subjective

feeling of comfort of the participants (Takayama and

Nass, 2008) are also frequently used. Nevertheless,

such approaches do not ensure that adaptation has in-

deed occurred. One approach to determine the time of

adaptation comes from McGehee, Lee, Rizzo, Daw-

son, and Bateman (McGehee et al., 2004). Their re-

sults show that drivers adapt within approximately

240s after starting the simulator scenario. Further-

more, McGehee et al. (McGehee et al., 2004) re-

ported an age-related increase in the steering variabil-

ity of older drivers, but no effect on adaptation rates.

In the context of steering adaptation pattern, Sahami

and Sayed (Sahami and Sayed, 2010) discussed the

processes of motor-cognitive skills, cognitive-mental

abilities, and the aspect of physiological transfer as

responsible for the time humans need to adapt to vir-

tual environments. Closely related to each other and

discussed as result of a lack of adaptation, simula-

tor sickness is described as a phenomenon including

symptoms such as headache, sweating, dry mouth,

drowsiness, disorientation, vertigo, nausea, dizziness,

and vomiting (Brooks et al., 2010b), which are sim-

ilar to those of motion sickness but typically less in

their appearance (Kennedy et al., 1993). Previous

studies reported experiences with simulator sickness

in 80% to 95% of participants and a drop-out rate of

5% to 30% (Stanney et al., 2002). In this context,

Balk, Bertola and Inman (Balk et al., 2013) discussed

nausea and nausea-related symptoms as most likely

described symptoms to fail to complete simulations.

Regarding sex-related differences in the occurrence

of simulator sickness, Garcia, Baldwin and Dworsky

(Garcia et al., 2010) reported a lower level of sim-

ulator sickness in males compared to females as a

function of ﬁxed-base versus rotating base platform,

Schweig, S., Liebherr, M., Schramm, D., Brand, M. and Maas, N.

The Impact of Psychological and Demographic Parameters on Simulator Sickness.

DOI: 10.5220/0006837300910097

In Proceedings of 8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2018), pages 91-97

ISBN: 978-989-758-323-0

which is in accordance with previous studies (Freund

and Green, 2006). In the context of age-related differ-

ences there is a common agreement that older adults

tend to be more susceptible to simulator sickness than

younger participants (Brooks et al., 2010b). These

ﬁndings are explained by Domeyer, Cassavaugh, and

Backs (Domeyer et al., 2013) based on the fact of a

lack of experience with simulated environmentsin the

elderly. Mullen, Weaver, Riendeau, Morrison, and

B´edard (Mullen et al., 2010) compared older adults

who failed to complete a simulated drive because of

simulator sickness and those who completed the sim-

ulation by using onroad drivingperformance,the Use-

ful Field of View test, the Attention Network Test,

and the Trail Making Test (A). Based on their results

the authors suggested that cognitive differences are

not associated with dropping out because of simulator

sickness. In contrast, Kawano et al. (Kawano et al.,

2012) reported a relation between visuospatial func-

tion and the onset of simulator sickness. For exam-

ple, Rizzo et al. (Rizzo et al., 2003) reported a 2.4

times higher simulator sickness rate for cognitively

impaired drivers than for their healthy counterparts.

A quantitative relationship between subjective sim-

ulator sickness and objective physiological measure-

ments of the central and autonomous nervous systems

has been reported by Min, Chung, Min and Sakamoto

(Min et al., 2004).

In the past, numerous theories tried to give a better

understanding regarding the cause of simulator sick-

ness. The most widely accepted theory – the sen-

sory conﬂict theory (Reason and Brand, 1975) – as-

sumes a conﬂict between or within sensory systems

responsible for the occurrence of simulator or mo-

tion sickness. This is in accordance with more re-

cent studies, which reported a discrepancy between

vestibular signals and other, primarily visual, infor-

mational inputs in simulator sickness (Kennedy et al.,

1993). In contrast, Riccio and Stoffregen (Riccio

and Stoffregen, 1991) suppose that congruent infor-

mation from sensory systems is unusual even in nor-

mal everyday tasks. Furthermore, it is assumed that

simulator or motion sickness occurs when people are

placed in novel environment in which effective ways

to maintain balance have not been learned (Duh et al.,

2004). Another theory comes from Treisman (Treis-

man, 1977). The evolutionary theory describes the

cause of simulator sickness based on the fact the hu-

man species has not had sufﬁcient time to adapt to the

relatively new modes of transportation and therefore

the body responds to conﬂicts in sensory information

(Money and Cheung, 1983). The Neural Mismatch

model – introduced by Reason (Reason, 1978) – de-

scribes the existence of a conﬂict between sensory in-

formation and one’s own experiences of a motion en-

vironment as responsible for the occurrence of simu-

lator or motion sickness.

As reported above, the occurrence of simulator

sickness is directly linked to the aspect of adapta-

tion. This becomes also evident in the consideration

of studies, which reporting decreased simulator sick-

ness symptoms with repeated exposure within and be-

tween days. For example, Domeyer, Cassavaugh and

Backs (Domeyer et al., 2013) reported fewer simu-

lator sickness symptoms of participants who expe-

rienced a two-day delay between an initial acclima-

tion to the driving simulator. This reduction has been

shown to persist up to a month or longer (Hu and

Stern, 1999). Within eight trials Mourant, Rengara-

jan, Cox, Lin, and Jaeger (Mourant et al., 2007) re-

ported an increase in simulator sickness from trial one

to ﬁve and a decrease from ﬁve to eight. Furthermore,

the authors pointed out the relevance of the environ-

ments used in the simulator.

Based on the previous ﬁndings, we hypothesize

an effect of the process of aging on the occurrence

of simulator sickness, the time of adaptation as well

as mental abilities. Gender related differences of the

time spent in the simulator are supposed. Regarding

the time spent in the simulator, we further assume dif-

ferences between the group who adapt to the simula-

tor and those who do not.

2 EXPERIMENTAL PROCEDURE

2.1 Participants

In total, 414 people (mean age = 61.69 SD = 12.66

years, ranging from 25 to 89 years, 153 women) par-

ticipated in the study at hand. All participants have

a driving licence and are still actively driving a car.

The 20 people aged from 25 to 50 represent a con-

trol group compared to the 50+ target group. After a

medical check, participants were excluded when they

reported neurological or cardio-vascular diseases but

also impairments in the ability to see or hear. Further-

more, people above 60 were tested by the DemTect-

Test and excluded even when they showed ﬁrst signs

of dementia (DemTect<9). The study was performed

in accordance with the ethical standards laid down in

the Declaration of Helsinki. All participants provided

written informed consent prior to the experiment and

were informed that they could end participation at any

time without reprisal.

SIMULTECH 2018 - 8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

2.2 Mental Abilities

2.2.1 D2 Test of Attention

The d2 Test is proposed as reliable measure of at-

tention and concentration (Brickenkamp, 1962). The

test is a one-page paper-and-pencil test, consisting

of 14 rows, each with 47 p and d characters (Brick-

enkamp and Zillmer, 1998). The characters have one

to four dashes that are conﬁgured individually or in

pairs above and/or below each letter. Participants are

asked to cross out all d with two dashes, regardless

of whether the dashes are above/below the d, or one

above and one below the d. Thus, a p with one or two

dashes and a d with more or less than two dashes are

distracters. For each row the time is limited to 20 sec-

onds in which participants should cancel out as many

targets as possible. No pauses are allowed between

trials. In agreement with previous studies, (Bates and

Lemay, 2004) showed that the d2 Test is an internally

consistent and valid measure of visual scanning accu-

racy and speed.

2.2.2 Trail Making Test (B)

The Trail Making Test (TMT) was originally de-

veloped as a component of the Army Individual

Test Battery and requires a variety of mental abil-

ities such as cognitive alternation/ﬂexibility, inhibi-

tion/interference control, working memory, mental

tracking, and attentional set-shifting. Within part B

of the TMT, participants should alternately connect

12 letters (A-K) and 13 numbers (1-13) on a page of

paper. For example, the ﬁrst number 1 is followed

by the ﬁrst letter A, followed by the second number 2

then second letter B and so on. The time for complet-

ing the task is recorded. Errors are directly pointed

out by the examiners. Therefore, error-correction in-

ﬂuences the time to complete a trail (Lezak, 1995).

Since its development, the test has been shown as a

robust measurement of intelligence (Waldmann et al.,

1992), neurological impairments (Reitan and Wolf-

son, 2004), but also declines related to the process of

aging (Wahlin et al., 1996).

2.2.3 Leistungspr¨ufsystem (German Intelligence

Test Battery) LPS-4

The Leistungspr¨ufsystem was developed by Horn

(Horn, 1983) in order to measure individuals’ level

of intelligence. The LPS-4 is one out of fourteen

sub-tests summarized in the Leistungspr¨ufsystem and

measures inferential thinking. Participants are faced

with 40 lines of numbers and letters on one page of

paper. They are asked to cross out the number or let-

ter that does not ﬁt in the logical order of each line.

The difﬁculty increases from line 1 to line 40. The

time to complete the test is limited to eight minutes.

2.3 Driving Simulator

For the determination of the connections between

mental performance, age, adaptation time and simu-

lator sickness the static driving simulator of the chair

of mechatronics was used. It consists of a close-to-

production vehicle of the compact class, which has

been extended by force feedback components for the

simulation of forces and torques. Among these, in

particular, the steering is to be mentioned, which rep-

resents one of the most important bi-directional in-

terfaces between driver and vehicle. The simulator

is located in a rectangular ”cave” (Figure 1) on the

ground without a movement platform. The simulated

vehicle environment is projected on the caves walls.

The ﬁeld of view of the driver outside the vehicle in

this arrangement is 180

◦

and thus includes the entire

vehicle front. The side mirrors of the vehicle are rep-

resented by screens, which also serve the visual rep-

resentation of the vehicle environment. In addition,

behind the rear window of the vehicle, a monitor is

placed, which is used for both the rear view and for

the reﬂection of the image in the interior mirror.

Figure 1: Driving simulator cave.

The driver inputs are read by means of the vehi-

cle CAN bus and used as inputs of a complete vehicle

simulation (Maas et al., 2014), which simulates a re-

alistic driving behavior. An electric car is simulated,

which results in an automatic gearing and very low

noise emissions. Furthermore, road users are repre-

sented in the vehicle environment who interact with

the simulated (EGO) vehicle (Maas, 2017).

Subjects are therefore in a simulation environment

whose usability is physically not different from a se-

The Impact of Psychological and Demographic Parameters on Simulator Sickness

ries car and thus has a very high degree of reality. The

visualization as well as the missing movement of the

vehicle on the other hand represent a strong simpliﬁ-

cation to the reality. Thus, both a high degree of im-

mersion, as well as a sufﬁcient deviation from reality

is given, which (as shown above) serves as a possible

cause of the simulator sickness.

The presented road scenario (Figure 2) includes

an area of 3 x 3 km and consists of inner-city areas,

rural routes as well as highways. With a total length

of over 70 km, which are merged into several loops,

the impression of an inﬁnite scenario without dead-

ends is created, which further supports the realistic

impression.

PSfrag replacements

inner city

freeway

country road

Figure 2: Driving simulator road scenario.

2.4 Procedure

Before starting, participants gave informed consent

after carefully reading the instructions. They were in-

formed that they could end participation at any time

without reprisal. Afterwards all participants under-

went a medical examination with main focus on the

suitability for driving in the simulator. Vision and

hearing but also cardio-vascular impairments (blood

pressure and electrocardiogram) were tested. Further-

more, participants were asked for a history of neuro-

logical, psychological, and orthopedic disorders. Af-

ter the medical examination, participants performed

the neuropsychological test battery. People above 60

started with the DemTect-Test. Afterwards all par-

ticipants completed the identical test sequence start-

ing with the d2 Test for attention and concentration,

followed by the Trail Making Test (B), and part four

of the Leistungspr¨ufsystem. Before starting with the

driving simulator participants had 5 to 15 minutes to

rest. Afterwards, they were instructed to the driving

simulator. Participants were informed that they can

stop driving at any time in case they feel sick or they

want to cancel for another reason. Next to technical

instructions (e.g., that it is a vehicle with automatic

transmission) participants were asked to choose inde-

pendently how and where to drive, within the virtual

scenario, without following a preceding car or a pre-

deﬁned route. Maximum driving time in the simula-

tor was 25 minutes, as 20 minutes of driving time is

planned for the following tests on the subject of assis-

tance systems.

2.5 Data Analysis

Numerous data were recorded during the trip. Using

Joshi’s algorithm (Joshi et al., 2017), the index of per-

formance (IOP) was calculated from the data. This in-

dex contains numerous criteria that provide informa-

tion on driving performance. The track deviationindi-

cates how precisely the driver manages to drive in the

middle of the road. In addition, the steering behav-

ior and activity of the pedals are evaluated. The IOP

increases rapidly during unconventional driving, e. g.

in the case of permanently large steering movements

in order to keep the vehicle on track or alternately

pushing the accelerator and brake pedals to their end

position. As soon as the participant had adapted the

system, the IOP stagnated or decreased (e.g. Figure

3). Otherwise, the IOP continued to rise (e.g. Figure

4). The temporal progressions of the IOPs were calcu-

lated from the stored data and examined for gradients.

All calculation of IOPs and their gradients were per-

formed automatically with MATLAB and displayed

graphically. In order to take into account the distance

travelled and the individual trafﬁc situations (trafﬁc,

trafﬁc lights, etc.), the determination of the adaptation

time was carried out by hand.

500

1,000

1,500

Time [s]

IOP (avg)

IOP adapted driving

IOP (avg)

gradient

Figure 3: Example average IOP of a well driving partici-

pant.

2.6 Statistical Analysis

Statistical analyses were carried out using SPSS

Statistics 24.0 for Windows. Homogeneity of vari-

ance was tested by using Levene’s test. The calcula-

SIMULTECH 2018 - 8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

Table 1: Mental ability.

Domain Test M SD Range

Driving simulator Time within the simulator [seconds] 1127.5 492.91 19-1716

Time of adaption [seconds] 549.97 280.68 53-1205

Mental abilities D2 [errors] 158.81 40.15 50-284

TMT (B) [seconds] 83.29 34.33 28-255

LPS-4 [number] 25.19 4.14 11-38

500

1,000

1,500

0.5

1.5

Time [s]

IOP (avg)

IOP non-adapted driving

IOP (avg)

gradient

Figure 4: Example average IOP of a bad driving participant.

tion of relationships between two variables was made

by means of Pearson’s correlations. T-test for inde-

pendent samples was used to investigate gender as

well as group (people who adapt to the system vs.

not adapt to the system) differences regarding the time

spent in the simulator. Effect size was tested by Co-

hen’s d.

3 RESULTS

Results indicated a mean time in the simulator of

1127.5±492.91 seconds, ranging from 19 to 1716

seconds. Ninety participants adapted to the system

within an average time of 549.97±280.68 seconds

(ranging from 53-1205 seconds). Mental ability was

represented by the D2, TMT(B), and LPS-4. D2 show

a mean error rate of 158.81±40.15. Participants com-

pleted the TMT(B) in 83.29±34.33 seconds and iden-

tiﬁed 25.19±4.14 errors in the LPS-4 (see Table 1).

Based on a sample size of 414 people (mean age

= 61.69 SD = 12.66 years, 153 women), ﬁndings re-

vealed signiﬁcant correlations between age and the

time within the simulator as well as mental abilities

(tested by the D2, TMT, and LPS-4). No relation

was identiﬁed between mental abilities and the time

spent in the simulator. The single components of

mental ability correlated signiﬁcant with each other

(see Table 2). Furthermore, results show a gender-

related effect in the occurrence of simulator sickness,

by indicating signiﬁcantly less time in the simulator in

women (M = 995.65 seconds SD 510.80) compared to

men (M = 1204.79 seconds SD = 466.01) (T(295.4) =

4.152, p <0.001, d = .428).

In most cases of premature abortion, the partic-

ipants complained of dizziness and nausea. A sur-

vey of participants based on Kennedy (Kennedy et al.,

1993) with similar modiﬁcations to Brooks (Brooks

et al., 2010a) was conducted after the study. The re-

sults have not yet been fully evaluated. The symptoms

did not appear immediately on all participants, but in

some cases minutes to hours after the study.

Table 2: Correlations of all participants.

2 3 4 5

1 Age -.103* .351** .475** -.387**

2 Time - -.054 -.069 .052

3 D2 - .470** -.429**

4 TMT - -.522**

5 LPS-4 -

*p ≤ .050 **p ≤ .010

Only 90 participants (mean age = 60.36 SD =

14.39 years, ranging from 25 to 89 years) out of the

414 showed an adaptation to the simulator system.

Here, results indicate no signiﬁcant correlation be-

tween aging and the time of adaptation. A signiﬁcant

mild correlation is shown between the performance of

the D2 and the time of adaptation. Results, regarding

mental abilities as well as the relations between single

tests for mental ability – as shown in the total sample

– are replicated in this smaller sample size (see Table

3).

Table 3: Correlations of participants who have adapted the

driving simulator.

2 3 4 5

1 Age -.150 .315** .461** -.364**

2 Time - -.222* -.051 .009

3 D2 - .401** -.352**

4 TMT - -.599**

5 LPS-4 -

*p ≤ .050 **p ≤ .010

In addition, people who adapted to the system

The Impact of Psychological and Demographic Parameters on Simulator Sickness

(M = 1395.12, SD = 287.90) have spent signiﬁcantly

longer in the simulator compared to those who did

not (M = 1053.16, SD = 512.17) (T(259.1) = -8.220,

p <0.001, d = .823).

4 CONCLUSIONS

The present study investigated the correlations be-

tween mental abilities, age, driving simulator adap-

tion and simulator sickness. Derived from the results,

shown in the previous section, it can be stated that:

• Age has an effect on both, mental abilities and

probability of simulator sickness occurrences,

• mental abilities do not have a signiﬁcant effect on

simulator sickness occurrences,

• the gender can have an effect on simulator sick-

ness occurrences and

• adapting to the simulator leads to a smaller prob-

ability of simulator sickness occurrences.

5 OUTLOOK

Starting from the point of the general inﬂuences on

simulator sickness described here, further possible

parameters can be identiﬁed in future work. Among

these properties, the properties of the virtual scenario

should be taken into account in particular. In this con-

text, it should be investigated to what extent the adap-

tation to challenging driving tasks correlates with the

cognitive workload.

Another challenging option is the consideration of

the subjective adaptation time. By determining this

parameter, existing algorithms can be extended or re-

placed by objectiﬁable methods.

The evaluation and analysis of the questionnaire

on symptoms, time, duration and intensity of the sim-

ulator sickness promises to provide interesting data

for future experiments.

Further tests with a modiﬁed simulator provide

comparative data and allow systematic improvement

of the simulations.

ACKNOWLEDGEMENTS

The studies presented here were carried out within the

framework of the research project ”ALFASY - Alters-

gerechte Fahrerassistenzsysteme”. This project was

funded by the European Regional Development Fund

(ERDF).

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The Impact of Psychological and Demographic Parameters on Simulator Sickness