D3.2: SIMULATOR SICKNESS MITIGATION STRATEGIES DISSEMINATION AND

COMMUNICATION PLAN

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement N. 723386 The content of this deliverable does not reflect the official opinion of the European Union. Responsibility for the information and views expressed therein lies entirely with the author(s)

WP3 SIMULATION

TASK 3.2 – Simulation of interactions, toward usable simulator platforms

D3.2 SIMULATOR SICKNESS MITIGATION STRATEGIES

Contract number: 732420 Project acronym: SIMUSAFE Project title: SIMULATOR OF BEHAVIOURAL ASPECTS FOR SAFER TRANSPORT DELIVERY DATE M33 Author(s): COU (Cyriel Diels, Allison Duncan) Partners contributed: ITCL, IFSTTAR, UPORTO Date: 28/02/2020 Version: 02 Revision: 01 Abstract: Simulator Sickness discussed for the case of SimuSafe. Includes general guidelines and a protocol for mitigation and recovery to be used during the pilots. After an internal submission in M23 (v1.4), this update is the final version, for submission to the EC. Status:  PU (Public)  PP Restricted to other program participants (including the Commission Services)  Restricted to a group specified by the consortium (including the Commission Services) (please specify the group)  Confidential, only for members of the consortium (including the Commission Services

1. DOCUMENT REVISION LOG

VERSION REVISION DATE DESCRIPTION AUTHOR 01 00 16/10/2018 First Draft COU Cyriel Diels 01 01 14/11/2018 Revision, comments and questions added. ITCL Javier María 01 02 07/12/2018 Revision, comments and questions UPORTO João Jacob COU Allison Duncan 01 03 15/02/2019 Revisions & Alex Stedmon COU Allison Duncan 01 04 02/04/2019 Final issues resolved ITCL Javier María Final formatting and quality check; ITCL Marteyn van 01 05 04/04/2019 Internal submission Gasteren 02 00 05/02/2020 Protocol update, overall review & updating COU Allison Duncan ITCL Marteyn van 02 01 28/02/2020 Final formatting and quality check Gasteren

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2. CONTENTS

1. DOCUMENT REVISION LOG ...... 3

2. CONTENTS ...... 4

3. EXECUTIVE SUMMARY ...... 7

4. INTRODUCTION ...... 8

5. SIMULATOR SICKNESS ...... 12

5.1. Introduction ...... 12 5.2. Prevalence of simulator sickness ...... 13 5.3. Assessment methods ...... 14 5.4. Simulator sickness theories ...... 15 Sensory conflict theory ...... 15 Subjective-Vertical conflict theory ...... 15 Evolutionary hypothesis ...... 16 Postural instability theory ...... 16 Eye movement hypothesis ...... 16

6. CONTRIBUTING FACTORS ...... 18

6.1. User characteristics ...... 18 Gender ...... 18 Ethnicity ...... 18 Experience with real task ...... 18 Age ...... 19 Physical and mental state ...... 19 6.2. System factors ...... 19 HMD vs Display ...... 19 Field Of View ...... 20 Flicker ...... 20 Transport delay ...... 20 Motion base ...... 21 Ambient temperature ...... 22 6.3. Task characteristics ...... 22

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Control ...... 22 Exposure duration ...... 22 Head and eye movements ...... 23 Visual stimulus characteristics - Kinematics ...... 23

7. MITIGATION STRATEGIES ...... 27

7.1. Independent Visual Background ...... 27 7.2. Screening ...... 27 7.3. Familiarisation ...... 28 7.4. Habituation ...... 29

8. MITIGATION GUIDELINES ...... 31

9. MITIGATION AND RECOVERY PROTOCOL ...... 33

10. REFERENCES ...... 35

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Figure Index Figure 1. Negative side effects in response to simulation and VR technologies are a function of a complex interplay between system user and task characteristics...... 9 Figure 2. SIMUSAFE simulators, from left to right: car, PTW, bicycle and pedestrian simulators...... 10

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3. EXECUTIVE SUMMARY Simulator sickness is an identifiable issue that may interfere with data collection during the SIMUSAFE project. The partners have identified this as a potential concern; this deliverable examines simulator sickness, its potential causes, and some possible mitigation steps the partners may take to minimise the potential for study participants experiencing simulator sickness.

Simulator sickness is thought to be triggered by the conflicts and inconsistencies between the different sensorial information sent to the brain by our visual, vestibular and proprioceptive systems. This is called the sensory conflict theory. The management of simulator sickness is challenging due to the many contributing factors which can be categorised into the individual, task, and system characteristics.

As evidenced by the many contributing factors to simulator sickness, there is no single solution or method to minimise the occurrence of simulator sickness. Effective simulator sickness management requires optimisation and adaptation of system and task characteristics, respectively, whilst taking into account user characteristics. Some mitigation strategies include implementation of an independent visual background, familiarisation, and habituation. Simulator sickness management required an integrated approach, and Section 8 lists 20 recommendations to help minimise the potential for simulator sickness.

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4. INTRODUCTION The SIMUSAFE project will make extensive use of various simulators (bicycle, motorbike, car) and Virtual Reality (pedestrian) setups. In the context of road safety research, simulation and Virtual Reality (VR) technologies provide distinct advantages in that they provide 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.

However, people frequently report negative side effects both during and after the use of these technologies. Amongst the most prominent side effects is simulator sickness, a form of motion sickness characterised by signs and symptoms such as nausea, headache, fatigue, and drowsiness. Although the exact prevalence and severity of simulator sickness is difficult to gauge, simulator sickness is widely considered to be the main limiting factor of VR and simulation technologies.

For the SIMUSAFE project, the occurrence of simulator sickness has several implications. Apart from being a considerable nuisance to those who will take part in the simulator studies, participant drop-out (i.e. participants unable or unwilling to continue to use the simulator due to experiencing sickness) will have direct consequences for the required staff, budget, time, and facility resources. Second, following exposure to simulations, participants may experience residual side effects and hence may present significant Health and Safety (H&S) concerns. This may be related to symptoms such as nausea, dizziness and headache, postural instability, hand-eye coordination, and general tiredness. It may require participants to stay at the research premises until symptoms have subsided and / or to be transported by taxi to and from the research premises.

Third, and most important for the direct aims of the SIMUSAFE project, users may adopt behaviours that may alleviate symptoms, for example, limiting head and eye movements, or even more significantly, adjusting their driving / riding behaviour in such a way as to avoid disturbing manoeuvres. Consequently, the behaviour displayed in a simulation may not be the same as that observed in the real world thereby compromising the behavioural validity of simulation. Since behavioural validity is the “raison d’être” of the SIMUSAFE project, it follows that the management of simulator sickness is critical.

It is important to emphasise that simulator sickness is a natural response to an unnatural environment. No matter how advanced the system or technology is, side effects will inevitably occur in response to unnatural environments, or better, environments we have not yet adapted to. In fact, higher levels of fidelity may exacerbate the problem. Hence, the acceptability and effectiveness of simulation and VR technology will depend on the manner in which it is employed and managed.

The management of simulator sickness is challenging due to the many contributing factors which can be categorised into the individual, task, and system characteristics. Sickness in response to simulation and VR technologies is not only polysymptomatic, it is also polygenic. Any mitigation strategy will have to take into account system, user and task characteristics, and consider the aims of the application under consideration.

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System

User Task

Figure 1. Negative side effects in response to simulation and VR technologies are a function of a complex interplay between system user and task characteristics.

The most appropriate simulator sickness mitigation strategy will depend on the context and aims of the research under consideration. With regards to the SIMUSAFE project, there are several considerations and constraints that will influence the mitigation strategy. Following the above described categorisation of contributing factors, the relevant constraint and considerations with regard to simulator sickness include the following:

 System characteristics

SIMUSAFE will have a range of different simulators. To facilitate the accessibility of simulation technology beyond the SIMUSAFE project, the project makes use of simulators solutions of reasonable fidelity and affordability. None of the simulators will incorporate a moving base, or motion platform, but will include realistic vehicle controls, large Field of View (FoV) displays, and near photorealistic virtual environments with advanced behavioural traffic models.

The car simulator is a fixed-base single seat setup with all primary driving controls (steering wheel, indicator, peddles, handbrake) available and employs three large LED displays to present the virtual environment (Figure 2, far left). The bicycle simulator will consist of a bike fitted out with various sensors to detect steering angle, speed, cadence, and pressure distribution with the virtual environment presented three LED displays (Figure 2, third from left – note the right display is not present). For the powered two-wheeler (PTW) simulator the Honda Rider Trainer, including complete handle bar and gears (Figure 2, second from left). Finally, the pedestrian simulator makes use of the Cyberith Virtualizer platform, which is an omnidirectional treadmill, in combination with an HTC Vive HMD (Figure 2, far right).

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Figure 2. SIMUSAFE simulators, from left to right: car, PTW, bicycle and pedestrian simulators.

 Task characteristics

The SIMUSAFE project focuses on urban scenarios. Of particular interest are left turns at intersections and roundabouts, two traffic scenarios that are overrepresented in accident statistics. From a mitigation perspective, it is noteworthy that both scenarios are relatively low speed but involve considerable lateral accelerations and rotations. As will be discussed in more detail below, changes in direction and speed (i.e. velocity) are one of the most important causes of simulator sickness. In other words, the task characteristics of interest in SIMUSAFE are amongst the most challenging.

During the naturalistic driving and riding studies in research cycle 1, participants will not be restricted in terms of the task and, hence, there will be little control over the motion and associated simulator sickness participants may experience. In the second research cycle (i.e. controlled environment), the range of manoeuvres will be controlled to a large extent and the scenarios can be tuned somewhat to avoid provocative motion scenarios. Finally, in the third research cycle, very specific manoeuvres and tasks will be researched which again will allow for a considerable amount of control in order to minimise the occurrence of simulator sickness.

The SIMUSAFE project will also incorporate multi-user scenarios, i.e. multiple road users interacting in the same virtual environment. The multi-actor scenarios are expected to be orchestrated to some extent but involve multi-user interactions that cannot be controlled and may lead to more provocative simulation scenarios compared to fully-scripted interactions.

 User characteristics

The simulators will have to be used by a large number of people who may not have used simulation and VR technologies before and will include a wide age range. Furthermore, opportunities to get used to the systems will be limited and therefore require intuitive, easy to use, and accessible simulators.

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During the third research cycle, several impairments will be evaluated including those caused by alcohol and drugs. It can be expected that simulator users under such altered conditions will become more susceptible to simulator sickness and mitigation strategies will become most critical in this third and final research cycle.

The above considerations and constraints are important when evaluating the different contributing factors and sickness mitigation strategies. There will be several trade-offs that have to be considered. The ultimate goal will be to find the balance between appropriate levels of behavioural validity, immersion or presence, simulator usability, and the prevention of simulator sickness.

In light of the above, this deliverable will first provide an introduction to simulator sickness, its aetiology, prevalence, and impact. It will then provide an overview of contributing factors of relevancy to the simulators and scenarios to be employed within the SIMUSAFE project. Finally, it will provide a summary of available mitigation strategies and recommendations to minimise the occurrence of simulator sickness in SIMUSAFE.

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5. SIMULATOR SICKNESS

5.1. Introduction

Normally, self-propelled motion in a natural environment does not lead to sickness in healthy individuals. However, when exposed to artificial or unnatural environments, such as being aboard a ship, watching a movie in a movie theatre, or driving a simulator, motion sickness may occur. Depending on the type of motion and environment, it is referred to as carsickness, airsickness, seasickness, Cinerama sickness, cybersickness, space sickness, visually induced motion sickness, and simulator sickness.

Upon exposure to simulated motion, some individuals may experience some of the range of symptoms associated with motion sickness. Those suffering from motion sickness may experience feelings of malaise and show symptoms like pallor, sweating, nausea, and in some cases vomiting. Motion sickness severity and symptoms tend to vary widely both between individuals and conditions. Individual differences in susceptibility have been suggested to be caused by numerous factors, including psychological and physiological factors such as age, gender, ethnicity, and field (in)dependence (e.g. Reason & Brand, 1975; Golding, 2006). There are however general trends in the occurrence and severity of motion sickness depending on the motion characteristics. Motion is not limited to actual physical motion, but may refer to motion of the visual scene alone or combinations of both.

The most widely accepted motion sickness theory is the sensory conflict theory (Reason & Brand, 1975). According to this theory, sickness is the result of conflicts and inconsistencies between the different sensorial information sent to the brain by our visual, vestibular and proprioceptive systems. In the fixed-base simulators proposed to be used within SIMUSAFE, for example, users are physically not moving while they are viewing a moving pattern displayed on a large field of view screen or display, inducing a sensation of self-motion (i.e. vection). This situation creates a sensory conflict as a consequence of the discrepancy between the motion information coming from the vestibular system (i.e. no self-motion) and the motion information coming from the visual system (i.e. self-motion). In sensitive individuals, these conflicting motion cues may lead to symptoms of motion sickness.

Despite differences in motion environments, there are some fundamental similarities across the different forms of motion sickness. It has long been suggested that individuals without a functioning vestibular system, the organs of balance in the inner ear, are immune to any form of motion sickness (Irwin, 1881; Kennedy et al., 1968; Money, 1970; Reason & Brand, 1975; Cheung et al., 1991; Johnson et al., 1999). Another observation concerns the fact that people who are in control of the motion (like car drivers and pilots) usually do not get sick, whereas passengers do (Rolnick & Lubow, 1991; Stanney & Hash, 1998). Further, with the exception of approximately 5% of the population, when people are repeatedly exposed to provocative motion, a reduction in motion sickness is reported. This adaptation, or better habituation, has been shown to occur with regard to both physical and visual motion environments (Reason & Brand, 1975; Hettinger and Riccio, 1992; Howarth & Hodder, 2008; Regan, 1995; Kennedy et al., 1990).

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In many ways, simulator sickness resembles the motion sickness classically experienced in, for example, ships, cars, and aeroplanes. Users experience signs and symptoms such as nausea, sweating, headaches, increased salivation, pallor, drowsiness, dizziness, stomach awareness, nausea and, in rare cases, vomiting (Lawson et al., 2002). Other additional symptoms that are unrelated to motion sickness have also been reported including general visual discomfort and eyestrain (Kennedy et al., 1990; Lawson et al., 2002) and are due to the use of synthetic displays.

There are a number of noteworthy differences between simulator sickness and motion sickness following exposure to physical motion. While studies in true motion sickness indicate that once a provocative stimulus has ceased symptoms generally disperse within ten minutes (Reason & Brand, 1975), symptoms experienced in simulators and VR systems have been reported for long periods after exposure, ranging from hours to even days (Howarth & Finch, 1999; Kennedy et al., 1990; Regan & Ramsey, 1994; Wertheim, 1999; Lawson et al., 2002).

The most prevalent aftereffect symptom is the continuation of simulator sickness, which may even happen with individuals who were not sick while inside the simulator. In addition, balance problems may occur not dissimilar to those experienced after debarkation following a sea voyage, a vague illusory feeling of being moved as if one were still aboard the ship at sea. This aftereffect may be quite discomforting and can last as long as a whole day (Kennedy et al., 1990; Regan & Ramsey, 1994; Lawson et al., 2002). In comparison to simulator sickness, these aftereffects are much less understood and often remain much less recognised. Although they seem relatively harmless, the possible negative effects on tasks such as car driving have never been investigated (Wertheim, 1999). It is clear that the occurrence of these aftereffects creates particular responsibilities and liability issues to those who work with individuals as trainers or investigators far beyond their actual contact with the individuals in the simulator (see Lawson 2002; Kennedy & Stanney, 1996).

5.2. Prevalence of simulator sickness

The exact prevalence and severity of simulator sickness is difficult to gauge. There are several reasons for this. First, both commercial and non-commercial simulator and VR operators are reluctant to publish data on negative side effects. With the exception of the US Department of Defense, it is uncommon for standardised records on side effects to be compiled. Furthermore, if side effects are recorded these tend to be on simulator sickness but other side effects are generally not assessed. To date, there are no central agencies or mechanisms with access to the necessary data to compile summary rates or discern relationships to simulation / VR characteristics. There is also no agreed upon operational definition of what constitutes simulator sickness and other negative side effects with furthermore different institutes and organisations using different assessment methods. Finally, with simulator sickness depending on a complex interaction between user, system and task characteristics, disentangling causative factors is difficult. In light of the above, a secondary aim of the SIMUSAFE project is to develop a cross-laboratory database to facilitate a better understanding of the occurrence of negative side effects.

Acknowledging the above difficulties and limitations, an attempt is made here to provide some estimates of the prevalence of simulator sickness to illustrate the size of the problem. This will be done

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on the basis of meta-studies that have been conducted in the late 80’s early 90’s, as well as some more recent driving simulator studies.

A study by Casali and Wierwille (1986) evaluated incidence levels in US flight simulators and reported dropout rates between 10 and 60% with vomiting accounting for less than 0.2% of cases. Watson (2000) described a number of simulator studies conducted between 1994 and 1998 on a high-fidelity driving simulator at the University of Iowa. Dropout rates were found to be highly dependent on the specific driving tasks and simulator configurations. With the motion base turned off, dropout rates in one study were found to be as high as 46% (females) and 21% (males). The addition of 90° turns within simulated scenarios was found to increase dropout rates from 4% to 50%. In a further study, 27% of the participants prematurely finished the trial due to simulator sickness.

Using a fixed-base driving simulator, Bertin et al. (2005) reported on the typical gradual increase of motion sickness over time with 67% of participants developing signs and symptoms of simulator sickness. Using a 6 DOF driving simulator, Groen and Bos (2008) showed that more than 50% of participants experienced problems with simulator sickness. Drop-out rates of 20% were reported for urban scenarios which reduced to 6% for constant speed motorway scenarios.

The author investigated the percentage dropout in 20 simulator studies (n=788) that were conducted using the TRL fixed-base driving simulator between 2006 and 2009. In total, the percentage dropout was 4.7%. Interestingly, large differences in percentages were observed depending on driving environment with the dropout percentage averaging at 2.3% and 8.9% for studies employing motorway and urban/rural scenarios, respectively. Findings from a large scale (n=478) fuel efficiency training program using moving-base truck simulators showed a drop-out rate as high as 25% (Reed et al., 2008).

Most recently, Forster et al. (2018) conducted a series of intercultural driving simulation studies using fixed base driving simulators in Germany, China, and the US with a total of 233 individuals. Drop-out rates ranged from 28% (China), 26% (Germany) to 13% (US). It is noteworthy that the simulator cockpit and display setup used in China and the US was similar to the proposed driving simulators for the SIMUSAFE project.

The Forster et al study illustrates that despite significant advances in simulator technologies it has not eliminated the problem of simulator sickness. It provides further support for the contention that simulator sickness cannot be regarded as an unfortunate consequence of an immature technology. It becomes apparent that in the context of simulator sickness management, technological improvements only cannot provide the answer. On the contrary, as will be discussed in the following sections, advances in simulation and CGI technology may exacerbate matters.

5.3. Assessment methods

There are multiple possible questionnaires available for researchers to use when screening for and evaluating simulator sickness. The Simulator Sickness Questionnaire (SSQ), the Motion Sickness Assessment Questionnaire (MSAQ), the Virtual Reality Sickness Questionnaire (VRSQ) and the Short Symptom Checklist (SSC) are the most common ones. Typically, participants are asked about their simulator sickness before, during, and after using the simulator. Use of an assessment method before

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using the simulator has two advantages—it screens out participants who have a history of simulator sickness as well as providing a baseline for comparison during and after the simulation.

5.4. Simulator sickness theories

Based on observations such as those described above, Reason and Brand (1975) postulated the sensory conflict theory, which is currently still the most widely accepted theory of motion sickness and will be discussed first.

Sensory conflict theory The root cause of motion sickness is generally regarded as the presence of sensory rearrangements, i.e. altered patterns of sensory signals within the human central nervous system (CNS), that are not expected based upon previous experience (Reason & Brand, 1975; Oman, 1982).

Our perception of self-motion is achieved by integrating the information from the different sensory systems involved in the computation of self-motion, most importantly the vestibular system, visual system, and somatosensory system (Howard, 1982). Under normal conditions, the information provided by these sensory systems is concordant. However, there are many situations where the information is discordant, and where an adequate sense of self-motion is not evident. For instance, when we are inside a ship below deck, our vestibular system registers the motion of the ship, whereas our eyes detect a stable environment. Conversely, in a fixed-base driving simulator or wide screen cinema (e.g. IMAX), changes in the visual world may lead to an illusory feeling of self-motion, also known as “vection” (Tschermak, 1931). This information does not however correspond to that provided by the vestibular and somatosensory system, which signal that the body is stationary. According to the sensory conflict theory (Reason & Brand, 1975), it is these kinds of sensory rearrangements that underlie the generation of motion sickness. Reason and Brand summarised their theory as follows:

“…all situations which provoke motion sickness are characterized by a condition of sensory rearrangement in which the motion signals transmitted by the eyes, the vestibular system and the non-vestibular proprioceptors are at variance not only with one another, but also with what is expected on the basis of past experience…” (Reason & Brand, 1975, p. 105)

Note that conflict existing between the patterns of sensory inputs is in itself insufficient to provoke motion sickness, since continued exposure to provocative motion will result in habituation as already mentioned above. Reason (1978) stressed that it is the crucial temporal comparison between sensed and expected patterns of stimuli that provides the link between sensory rearrangement and the onset of motion sickness.

Subjective-Vertical conflict theory The Subjective-Vertical conflict theory can be regarded as a simplification of Reason’s sensory conflict theory. It states that all types of motion sickness have only one underlying conflict in common, which is conflict concerning the subjective vertical (the internal representation of gravity): “…all situations which provoke motion sickness are characterised by a condition in which the sensed vertical as

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determined on the basis of integrated information from the eyes, the vestibular system and the non- vestibular proprioceptors is at variance with the subjective vertical as predicted on the basis of previous experience…” (Bles et al., 1998).

The theory has been developed into a mathematical model and has been shown to predict with high accuracy the level of motion sickness based on vertical heave motion characteristics in particular. However, the model in its current form has a profound vestibular basis and cannot readily explain some findings with regard to motion sickness that occurs following exposure to certain moving visual scenes (e.g. Stern et al., 1989, 1990).

Evolutionary hypothesis Although our understanding of the aetiology of motion sickness is ever increasing, the functional significance of the onset of motion sickness symptoms is still a matter of debate. According to Treisman’s (1977) evolutionary hypothesis, motion sickness is a tool for survival and the CNS misinterprets the sensory conflict or neural mismatch caused by motion as evidence that the body has ingested a toxin. Treisman proposed that since the systems involved in controlling movement, including eye movements, and in determining the location of the body in space, are complex, in action continuously, and are susceptible to even a minor degree of disruption, they constitute an ideal warning system for detecting early central effects of neurotoxins. However, considering the relatively long time required for a toxin to affect central vestibular mechanisms, vomiting is unlikely to be useful in removing toxins from the gastrointestinal tract (Yates et al., 1998). Alternatively, Benson (1988) suggested that the emetic response may just be a design defect, which, in an evolutionary time scale, has only recently become apparent with the use of mechanical aids to transportation.

Postural instability theory According to the postural instability theory (Riccio & Stoffregen, 1991), motion sickness results from prolonged instability in the control of posture. The theory states that prolonged postural instability is the cause of motion sickness, and that reductions in the demands on postural control will reduce the incidence and severity of motion sickness. However, to date, no convincing support for the postural instability theory has been provided (Smart et al., 2002; Warwick-Evans et al., 1998; Akiduki et al., 2003). Harm (2002) further pointed out that the theory is unable to explain why labyrinth defective individuals do not get motion sickness, and does not provide a clear explanation of why postural instability should actually cause motion sickness.

Eye movement hypothesis Ebenholtz et al. (1994) proposed that eye movements may play a causal role in the development of motion sickness, a suggestion based partly on the observation that anaesthesia applied to the extraocular muscles (retrobulbar anaesthesia) produces a significant reduction in the incidence of emesis and nausea after strabismus surgery (Houchin et al., 1992). Their hypothesis is based on the premise of a specific neural route between the vestibular system and vagal nuclei mediated by eye movements. That is, afferent signals from vestibular-mediated eye movements (e.g. traction of the extra-ocular muscles mediated by the vestibular nuclei during optokinetic nystagmus) affect the vagal

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nuclei, resulting in motion sickness. Hence more complex eye movements will produce more afference and, therefore, more motion sickness symptoms.

Whereas some studies have found the frequency of horizontal nystagmus to be positively correlated with the severity of motion sickness, the inability to dissociate illusory self-motion (vection) from nystagmus makes it impossible to evaluate the hypothesis (Flanagan et al., 2002; Hu et al., 1997; Hu & Stern, 1998; Stern et al., 1990). It was further pointed out by Hu et al. (1997) that a fast frequency of eye movements in seasickness, carsickness, and space sickness, is not apparent. Thus far, there have been no studies that provided convincing support for the eye movement hypothesis.

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6. CONTRIBUTING FACTORS Simulator sickness incidence may vary widely. This may not be surprising considering the many contributing factors related to the individual, task, and system characteristics. Subsequently, simulator sickness has been described as not only being polysymptomatic but also polygenic (Howarth & Costello, 1996; Kennedy & Fowlkes, 1992; Kolasinski, 1995; Nichols & Patel, 2002). In the following sections, user, system, and task characteristics will be discussed. Each of the contributing factors will be discussed in the context of the SIMUSAFE project constraints and considerations.

6.1. User characteristics

Gender Individual characteristics that have frequently been associated with the occurrence of simulator sickness as well as true motion sickness include gender. Whereas the underlying mechanisms remain elusive, women tend to be more susceptible than men (Clemes, 2004; Golding et al., 2005; Grunfeld & Gresty, 1998; Jokerst et al., 1999; Kennedy et al., 1995; Reason & Brand, 1975; Turner et al., 2000). In a recent large scale (n=256) driving simulator study, the total SSQ score for female participants was found to be 1.7 times as high compared to male participants (Diels & Parkes, submitted).

Ethnicity Asian individuals have consistently been found to be more susceptible than Caucasian or Afro- American individuals (Klosterhalfen et al., 2005; Stern et al., 1993; Stern et al., 1996; Diels et al., 2007). Whether this reflects cross-cultural differences in item responses or biological predispositions is currently unclear, however (Klosterhalfen et al., 2005).

Experience with real task Within the field of aviation, it is a well-established finding that larger experience with the real world task renders individuals more susceptible when performing the same task in a simulated environment (e.g. experienced pilots vs. novices) (Kennedy et al., 1987; Kennedy et al., 1988). This can be understood in the context of the sensory conflict theory in that real-world experience with the sensory aspects of the particular task might lead to greater sensitivity to discrepancies between expected and sensed self-motion signals. Subsequently, one might expect a positive correlation between individuals’ driving experience (e.g. expressed as annual mileage) and simulator sickness. Recent results (Diels & Parkes, submitted) suggest an opposite relationship however, whereby individuals with a higher annual mileage report significantly lower levels of simulator sickness. Similar results were reported by de Winter & Kuipers (2009). Although it is not clear why this would be, it does suggest that the driver’s “internal vehicle-dynamics model” is already well established by the time they pass their driving test and that additional driving experience does not render individuals more susceptible possibly because no significant refinements of the internal model take place. It is currently however unclear whether the same holds true for professional drivers of emergency or high-performance vehicles as the vehicle motion profiles differ significantly from those experienced during everyday driving.

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Age Not surprisingly, experience is often confounded with age, which makes it difficult to gauge the relationship between age and simulator sickness. With regard to common, frequently encountered motion stimuli, it is known that tolerance to sickness-inducing stimuli is built up through experience over the years. Susceptibility is greatest between the ages 2 to approximately 12 years with a progressive increase in tolerance over the next decade (Reason and Brand, 1975). However, the same reasoning does not hold true for provocative motion environments encountered in simulators as these tend to be experienced infrequently, if at all. Interestingly, the relationship between age and simulator sickness appears to be the reverse to that observed for other forms of motion sickness. In a recent helicopter simulator study, Johnson (2007) showed that aviator age was significantly and positively correlated with sickness scores, even after the effect of total flight hours was held constant. Although there appear to be no studies in which driver experience is controlled for, Hein (1993) observed that in driving simulators, older drivers also tend to be more susceptible than younger drivers (Hein, 1993). On the basis of informal reports, these findings are in line with observations from various European driver simulator groups. In the above-mentioned large-scale study using the TRL driving simulator (Diels & Parkes, submitted), an illustrative finding was that 7 out of the 256 individuals who terminated the study prematurely were all female and between 51 and 67 years of age.

Physical and mental state It is a common observation amongst users of driving simulators that individuals who are not in their usual state of fitness tend to be more susceptible to simulator sickness (see also Kennedy et al., 1987). Although no formal studies have been conducted to explore the relationship between simulator sickness and such factors as fatigue, hangover, emotional stress, head colds, ear blocks, etc., it has been suggested that individuals who are not in their normal state should be excluded from simulator use (e.g. Kennedy et al., 1993). Although the underlying mechanisms are currently poorly understood, these various factors may render individuals more susceptible and exacerbate the effects of simulator sickness.

6.2. System factors

There are many system characteristics that have been associated with the occurrence of simulator sickness. These should not however be considered in isolation as most of them interact with task characteristics. Where appropriate, these interactions will be discussed.

HMD vs Display Depending on the context, the use of different terms may be justified. For example, it has been shown that VR setups incorporating Head Mounted Displays (HMD) and CAVEs tend to produce more eyestrain and nearly 2.5 times more sickness in comparison to simulators (Kennedy & Stanney, 1997). However, to avoid confusion and in acknowledgement of the fact that many contributing factors are of relevance irrespective of the specific platform, we here use the term “simulator sickness” only. Simulator sickness is thus defined as a polysymptomatic syndrome that, while resembling motion

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sickness, affects people with a range of symptoms including dizziness, nausea, sweating, pallor, and vomiting with no single symptom necessarily dominant (Helland et al. 2016; Kolasinski 1995).

Field Of View Field Of View (FOV) has consistently been shown to be an important factor in the occurrence of simulator sickness. A larger FOV not only induces a stronger feeling of self-motion (Allison et al., 1999; Dichgans & Brandt, 1978), but also leads to higher levels of simulator sickness (DiZio & Lackner, 1997; Harvey & Howarth, 2007; Ijsselsteijn et al., 2001; Kennedy et al., 1989). Lin et al. (2002) recently compared the level of simulator sickness in a driving simulator at four levels of FOV (60˚, 100˚, 140˚, and 180˚). The level of simulator sickness consistently increased with increasing FOV but approached an asymptote for FOVs beyond 140˚. Since the level of immersion and “presence” is also positively related to the FOV, a trade-off between the level of presence and simulator sickness is created (Ijsselsteijn et al., 2001; Lin et al., 2002). The issue is further complicated by the fact that limited peripheral vision due to a restricted FOV has been shown to have an effect on various aspects of driving behaviour including lateral control, speed perception, time-to-contact estimates, reaction times, braking behaviour, and fixation duration of eye movements (e.g. Kaptein et al., 1996; Kemeny & Panerai, 2003). It is therefore recommended to use as narrow a FOV as required by the task.

Flicker Flicker has been found to induce eye fatigue, simulator sickness, and to be distracting (Pausch et al., 1992; Harwood & Foley, 1987) and is largely a function of the refresh rate and update rate of the image buffer. Greater flicker may be apparent with high luminance, wide FOV, and high scene detail as these factors extend refresh and update rates. Since the peripheral visual system is more sensitive to flicker than the fovea (Boff & Lincoln, 1988), the likelihood that flicker will be perceived increases when the FOV increases. In particular, this large-field flicker can be interpreted as background motion, which can exacerbate the visual-vestibular conflict resulting in simulator sickness (Kennedy et al., 1990; Palmisano et al., 2003). The effects of flicker can be reduced by narrowing the FOV, reducing luminance levels, and reducing the load on the simulation hardware and software, for example by using simulations with lower scene complexity.

Transport delay Transport delay refers to the amount of time it takes to detect an operator input, process the new state of the simulator based on the input, and return to the operator the resulting changes in the state of the simulation. Following a cue conflict approach, transport delays may cause temporal discordances between visual and vestibular motion cues. Increases in delays between information input to, and visual and motion output from, the simulator, have repeatedly been shown to exacerbate simulator sickness (DiZio & Lackner, 1997; Draper et al., 2001; Howarth & Finch, 1999; Regan, 1995; So, 1994; Kennedy et al., 1990; Zaychick & Cardullo, 2005). In addition, in driving simulators, delays can also cause operators to perform self-induced steering oscillations that can exacerbate the problem through increases in visual artefacts caused by yaw rotation in the display.

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To compensate for delays, several algorithms have been developed using short-term predictions of position and orientation, based on the assumption that orientation and driving speed do not change during the calculation process (e.g. Hogema, 1997; Dagdelen et al., 2002; Salaani et al., 2003).

The aforementioned study by Zaychick and Cardullo (2005) indicated that delays as short as 33 msec may already degrade performance. Although the effects of delays may vary depending on the specific platform and task characteristics and requirements, it is clear that shorter delays are better than longer ones. In addition, it is important to avoid varying delays. Even though individuals can eventually adapt to temporal distortions, it is a well-known fact that adaptation is severely hampered when these distortions are inconsistent (Welch, 1986). Varying delays may occur when the frequency response of the cueing subsystem is not frequency independent (Papelis et al., 2005).

Motion base Vestibular motion cueing has been shown to increase the behavioural validity of simulators with regard to braking, cornering, steering and speed control when compared to fixed-base simulators (e.g. Siegler et al., 2001; Reymond et al., 2001; Wierwille et al., 1983; Kemeny & Panerai, 2003). In addition, it can be expected to enhance face validity which, mediated by an individual’s motivation, may indirectly contribute to behavioural validity. However, the incorporation of vestibular self-motion cues by means of a motion-base and/or vibration platform may also increase the likelihood of simulator sickness (e.g. Sharkey & McCauley, 1992; Slick et al., 2005). Whereas vestibular cueing partly compensates for the lack of physical motion, theoretically reducing the degree of sensory conflict and subsequent simulator sickness, partial and incorrect rendering of visual and vestibular self-motion cues may in fact have an aggravating effect. Stoffregen and Bardy (2001) argued that the perceptual system is sensitive to information redundancy and tends to be more disturbed by perceptual incoherence than by the lack of stimulation of a usually concomitantly solicited sensor.

Watson (2000) discussed a number of studies that were conducted on the high-fidelity driving simulator at the University of Iowa. The results for scenarios with and without motion were mixed and showed an interaction effect with driving scenario and FOV. Overall, it was found that no motion was preferred above inappropriate or limited motion. Motion that was perceived as good was however referred for motion-intense scenarios and for systems with a wide FOV.

Curry et al. (2002) compared sickness levels between the fixed- and moving-base driving simulators. Despite similar driving scenarios (motorway environment) and exposure times (30-40 min), the level of simulator sickness was significantly lower in the moving-base simulator. Although these results were attributed to the positive effect of the moving-base, the results are inconclusive and may have been caused by confounding factors including differences in the visual system between the two simulators.

Slick et al. (2005) showed that the motion-based driving simulator was rated more realistic than the non-motion-based simulator. However, it was also found that participants in the motion-based simulator reported higher levels of simulator sickness than in the fixed-base simulator.

It can be concluded that the addition of physical motion may not always be beneficial. Especially if the motion cues are not tuned correctly, simulator sickness may be exacerbated in moving-base simulators.

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Ambient temperature Although strictly speaking a system characteristic, the environmental thermal conditions of the simulation or VR facility is an important factor to take into consideration, reports of discomfort and motion sickness increase with increased temperature (Pausch et al., 1996). Current recommendations are that the cockpit environmental temperature be kept under 21°C (Klüver et al., 2015) but there is some debate in the literature about the actual impact ambient temperature has on simulator sickness.

6.3. Task characteristics

Acknowledging the importance of visual information in the generation of simulator sickness, task characteristics can be subdivided into those that have a direct effect on the visual scene motion (e.g. aggressive driving manoeuvres and cornering) and those that are not directly linked to visual scene motion. In the below the latter will be discussed first, followed by visual stimulus characteristics and their relationship to simulator sickness and associated task characteristics.

Control It is commonly reported that drivers of cars and pilots of aircraft are usually not susceptible to motion sickness despite the fact that they experience the same motion and visual scenes as their passengers (Geeze & Pierson, 1986; Reason & Brand, 1975; Rolnick & Lubow, 1991). In a more recent study, this was also shown to occur in VEs whereby individuals who were moved passively through a simulated building reported more sickness than those who were able to affect their movements themselves using a joystick (Stanney & Hash, 1998). This moderating effect of control on the generation of motion sickness symptoms has typically been attributed to the presence of muscular activity resulting in a concomitant efference copy. This efference copy is subsequently used to activate an internal model and is thought to facilitate the habituation process (Oman, 1982, 1991; Reason, 1978). Passive drive- through scenarios should therefore be avoided.

Exposure duration A general finding is that during exposure to a provocative environment the degree of motion sickness steadily increases over time. With regard to simulator sickness, sickness levels are generally higher for studies with longer simulator drives (Kennedy et al., 2000; Reason & Brand, 1975; Watson, 2000). Data from a large (n > 1000) simulator survey showed a correlation of r = 0.5 between average sickness and length of exposure in sixteen different simulators (Kennedy & Fowlkes, 1992). For this reason, exposure time should be limited until adaptation/habituation has occurred (see also McCauley and Sharkey, 1992). It is currently however unclear what the optimum session duration is. Put differently, how short is a “short” duration (Kennedy et al., 2000)? Although there is currently no duration curve to determine the optimum session duration, it is the experience of various European driving simulator groups that individual driving sessions of 5 minutes or less tend not to provoke simulator sickness. Within the SIMUSAFE project, additional exploration around duration may be a further contribution to the knowledge base.

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Head and eye movements Head movements have long known to be associated with motion sickness through the mechanisms of Coriolis and pseudo-Coriolis stimulation. Coriolis stimulation occurs when the head is tilted out of the axis of rotation during actual body rotation (Dichgans & Brandt, 1973). Pseudo-Coriolis stimulation occurs when the head is tilted as perceived self-rotation is induced from visual stimuli (Dichgans & Brandt, 1973). Head movements have been found to significantly increase the level of sickness during circularvection using optokinetic drums (Dichgans & Brandt, 1973; Tiande & Jingshen, 1991). Recent findings suggest that gaze position may also affect the level of simulator sickness. When exposed to optic flow patterns simulating oscillating fore-and-aft motion, gaze positioning away from the focus of expansion were associated with higher levels of simulator sickness (Diels et al. 2007). These results suggest that head and eye movements should be limited, especially during initial sessions in which individuals may be particularly prone to simulator sickness. This can be achieved by avoiding scenarios in which individuals are required to scan the visual environment extensively.

Visual stimulus characteristics - Kinematics Apart from the above factors which can be thought to affect simulator sickness directly, most task characteristics influence simulator sickness indirectly in that operator control behaviour affects the visual stimulus for self-motion. For example, Kolasinski (1995) identified altitude above terrain, global visual flow, unusual manoeuvres, rate of linear and angular acceleration, self-movement speed, vection, and type of application as further task characteristics. Of course, these factors are not independent and vection, for example, depends on the global visual flow rate which in turn is a function of altitude above terrain, rate of linear and angular acceleration, self-movement speed, manoeuvres, and type of application (i.e. ‘far’ vs. ‘near’).

Despite the fact that simulator sickness (particularly in fixed-base simulators) can be largely ascribed to the visually induced perception of self-motion, or vection, surprisingly few studies have directly addressed the question how visual stimulus characteristics (and thus associated driving tasks and manoeuvres) relate to simulator sickness. In the widely cited paper by Kolasinski (1995), only 1 out of 39 reviewed studies directly addressed this question (i.e. Sharkey & McCauley, 1991).

Global optic flow rate

Sharkey and McCauley (1991) noted that the level of sickness increased with increasing global visual flow rate, i.e. the rate or speed at which objects flow through the visual scene. Based on these findings, the authors recommended that self-movement in a VE should be at low speeds. Since the global optic flow rate is inversely related to altitude (i.e. eye height), lower altitudes result in higher global visual flow rates. Consequently, it was recommended that simulated self-motion should be at high altitudes above the terrain in order to limit the occurrence of adverse symptoms. Further support for the effect of global optic flow rate comes from optokinetic drum and VR studies, which also have shown that simulator sickness tends to increase with increased global flow rates (Hu et al., 1989; So et al., 2001).

Sharkey & McCauley (1991) further recommended that tasks requiring high rates of linear or rotational acceleration should be avoided or kept brief until full habituation to the virtual environment was

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achieved. Several studies have indeed shown that it is the change in image velocity (i.e. acceleration) rather than the velocity as such that determines the likelihood of simulator sickness occurring (Bubka et al., 2006; Bonato et al., 2005, Diels & Howarth 2005). In line with this, Watson (2000) also found that sickness rates in the Iowa driving simulator were significantly higher in scenarios with intersections than in those without intersections. In fact, the inclusion of intersections had a dramatic effect on dropout rates which increased from 4% to nearly 50%.

These effects can be understood considering that the vestibular system signals motion during accelerations only and returns to its resting position at constant velocity and acceleration (Howard, 1982). Visually induced motion at constant velocity induces little or no motion sickness as the resulting motion signals are consistent with naturally occurring conditions. This also explains observational reports of low level of simulator sickness in driving simulators using constant velocity driving scenarios such as driving on a motorway. Following the same rationale, Sharkey & McCauley (1991) further noted that abruptly freezing the simulation and "flying" backwards should be avoided. Frank & Casali (1986) further recommended that situational reset (i.e. rapid forward or backward resetting in time of the scene) should be avoided.

Spatial frequency

Increased spatial frequency (i.e. scene complexity) increases the load on the simulation hardware and software and this may increase the amount of flicker and may result in a poorer graphics update rate. As discussed above, flicker and slow or variable graphics update rates have been shown to provoke simulator sickness (Pausch et al., 1992). In addition, spatial frequency has also been shown to have a more direct effect on simulator sickness. Hu et al. (1997) investigated the effect of spatial frequency by covering the inside of an optokinetic drum with 6, 12, 24, 48, and 96 pairs of black and white stripes whilst keeping rotation speed constant at 60°/s. It was found that the intermediate spatial frequency of 24 stripes caused maximum simulator sickness. So et al. (2001) also showed that simulator sickness within a virtual environment increased with increasing spatial frequency/scene complexity and found a strong correlation between spatial frequency and the level of simulator sickness. These findings suggest that unless specifically required for the successful completion of a task, scene complexity should be minimised. As a corollary, highly detailed visual databases should be avoided where possible.

Pictorial realism

Kennedy et al. (2001) investigated the effect of pictorial/scene realism on simulator sickness by covering the inside of an optokinetic drum with patterns that were believed to be more realistic than the vertical black-and-white stripes commonly used. Although not affecting the sensation of motion, simulator sickness was found to differ significantly between the different patterns. Sickness was moderate with wood panelling and waves, much greater with clouds, and negligible with dots. It was suggested by the authors that the use of abstract stimuli would reduce the likelihood of simulator sickness whilst preserving the realistic perception of self-motion.

In a similar vein, Bonato et al. (2004) compared the level of simulator sickness during optokinetic drum rotation with the inside wall covered with 1) alternating black-and-white stripes, 2) grey stripes having different luminance values, and 3) chromatic stripes (white, red, yellow, black, green, and blue) that

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approximately matched the luminance values of the stripes in the grey condition. The chromatic condition was found to result in significant shorter onset times and higher sickness scores compared with the other conditions with the inside wall covered in black-and-white or grey stripes. Bonato and co-workers argued that chromaticity may affect how much an observer's visual environment appears to be stationary, perhaps because chromaticity is such a common feature of the stationary environment in which the visual system evolved. They argued that this may have increased the disparity between visual and vestibular inputs resulting in the elevated levels of simulator sickness observed. Thus, as for spatial frequency discussed above, in terms of simulator sickness it may be beneficial to use less detailed and more abstract and visually impoverished simulated environments. These findings argue against the current technology-driven trends towards ever increasing realism and fidelity and further research is required to further our understanding of the relationship between fidelity, validity, and simulator sickness. The SIMUSAFE partners recognize this is a complex area, and that within the project further work may be required to consider the potential influences of hyper- realism and its impacts on the participant experience.

Rotation axis

Simulator sickness can occur in response to image motion in any of the three (yaw, pitch, roll) rotational axes (Cheung et al., 1991; Hu et al., 1997; Kennedy et al., 2001; Stern et al., 1990). Tiande and Jingshen (1991) compared the level of simulator sickness during pitch, roll, and yaw rotation and found pitch and roll motion to be most provocative. This was also found in a study by Lo and So (2001). These findings may not be surprising considering that during yaw vection at constant velocity there is little or no visual-vestibular conflict present after the endolymph within the semicircular canals have returned to their resting position. For true motion about a horizontal axis, on the other hand, the otoliths continuously signal a rotating gravitational vector, even after the canals have ceased to respond. Therefore, during roll and pitch motion there is a continuous conflict between visual and (expected) vestibular signals. These findings are also in line with Kennedy et al.’s (1996) observations that roll motion in particular correlated significantly with the overall level of sickness during flight simulation involving complex motion scenarios. Thus, in order to minimise simulator sickness, driving scenarios containing rotational motion should be avoided particularly if these are not required for the specific research question or training goal at hand. Furthermore, pitch motion resulting from rapid decelerations (i.e. braking) can be particularly provocative and visual tilt should be applied with care.

Temporal frequency

With regard to the nauseogenicity of physical motion, the most important physical characteristics include the frequency, and less reliably, the acceleration and amplitude of the motion (Griffin, 1990; Guignard & McCauley, 1990). In laboratory studies using 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 (e.g. Bos & Bles, 1998; Donohew & Griffin, 2004; Golding & Markey, 1996). This frequency range is consistent with what is known about the provocative motion profiles of transport systems associated with motion sickness including ships, trains, aircraft, and cars (Guignard & McCauley, 1990; Lawther & Griffin, 1988). It has been suggested that the predominant frequency of oscillation of a visual display also plays an important role in the generation of simulator sickness (Kennedy et al., 1996), and that, similar to true motion sickness, imposed visual motion at a frequency around 0.2 Hz is

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most provocative (Hettinger et al., 1990). Using a fixed-base driving simulator, Lin et al. (2005) exposed stationary observers to optic flow patterns simulating constant velocity linear motion in the fore-and- aft axis combined with oscillating roll motion at three different frequencies, 0.035, 0.080, and 0.213 Hz. Rather than 0.2 Hz, simulator sickness was found to be highest at the mid-frequency range, i.e., 0.080 Hz. However, using linear rather than angular visual motion patterns, Diels and Howarth (2006) showed that within the range 0.025 - 1.6 Hz, simulator sickness was found to peak at 0.2 Hz. Similarly, with individuals exposed to the optokinetic equivalent of OVAR (Off Vertical Axis Rotation), Golding et al. (2007) also showed 0.2 Hz to be more provocative than lower 0.05 or higher 0.8 Hz frequencies. These results suggest simulator sickness to show a frequency dependence not dissimilar to other forms of motion sickness (see Griffin, 1990).

Future research may further benefit from the identification of visual stimulus characteristics that are most conducive to simulator sickness. This has already been shown to be a successful approach with regard to seasickness. Systematic studies into the relationship between motion profiles aboard ships and subsequent laboratory studies have shown oscillating motion along the vertical axis at around 0.2 Hz to be the main cause of seasickness (Griffin, 1990). This has subsequently led to the development of a Motion Sickness Dose Value (MSDV) for predicting seasickness based on the vertical motion of vessels (BSI, 1987). This information has been used successfully in the design process, which has led to the construction of transport systems that are less provocative of motion sickness. Following the same rationale, identification of visual stimulus characteristics that are most conducive to simulator sickness may provide valuable information. First, it may create a better understanding of the aetiology of simulator sickness, and secondly, identification of dominant axes and motion profiles allows for the prediction of simulator sickness. Ultimately, it may be possible to develop a simulator or ‘Cyber Sickness Dose Value’ (So, 1999).

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7. MITIGATION STRATEGIES As evidenced by the many contributing factors to simulator sickness, there is no single solution or method to minimise the occurrence of simulator sickness. Effective simulator sickness management requires optimisation and adaptation of system and task characteristics, respectively, whilst taking into account user characteristics. In this section, four different mitigating strategies are discussed that are independent of system and task characteristics.

7.1. Independent Visual Background

It has been suggested that providing an independent visual background (IVB) may provide an effective means to reduce the level of simulator sickness (Prothero et al., 1999). The premise behind the effect of an IVB is the idea that motion sickness does not arise from conflicting motion cues per se, but rather from conflicting rest frames selected from the available motion cues. Since the visual rest frame is heavily influenced by the visual background, it is suggested that the provision of an IVB consistent with the inertial rest frame may reduce simulator sickness. This would also be the case when the motion cues provided by the simulator are not consistent with the inertial rest frame. An initial VR study provided support for the effect of an IVB on simulator sickness (Prothero et al., 1999). More recently, implementation of the IVB in a driving simulator was also shown to reduce the level of simulator sickness (Duh et al., 2004). Although these results suggest the addition of an IVB to be an effective means to reduce simulator sickness, to the best of our knowledge, an IVB has not been implemented elsewhere and further research is required. An important consideration is the nature of the IVS in that an overlaid grid that is perceived as foreground is likely to increase simulator sickness (Howard & Howard, 1994). At the moment, this is outside the current scope of the SIMUSAFE project, but it should be noted and explored if resources and/or time allow this.

7.2. Screening

The ability to identify susceptible individuals is of relevance for a number of reasons: i) susceptible individuals may be exposed to special habituation programs ahead of time, ii) it may be necessary to design special scenarios or interfaces to reduce the prevalence of simulator sickness, and iii) exclusion of highly susceptible individuals reduces the risk of compromising experimental studies due to participant drop-out (Kennedy et al., 2001).

Kennedy and colleagues (1990) conducted a meta-analysis of the predictive validity of several predictors of susceptibility to various forms of motion sickness including simulator sickness and concluded that highest predictive validities were obtained with operational measures, followed by laboratory stimulations (provocative tests), motion sickness history, psychological factors (personality and perceptual style), and physiological measures (autonomic and sensory function).

Despite their superior predictive validity, operational measures and laboratory simulations are of limited practical value. The main disadvantages are their provocative nature and the obvious logistical issues. In the light of cost-effectiveness, convenience to the individual, and high measurement

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reliability (± r = 0.80 (Kennedy et al., 1990)), assessment of an individual’s motion sickness history provides the most useful and practical method for predicting motion sickness.

Over the last decades, a number of questionnaires have been developed to assess an individual’s motion sickness history. These questionnaires share common elements indicating the types of motion or vehicles that have made the individual sick, the frequency of sickness, and the severity of symptoms. The most widely used and validated questionnaires are the Pensacola Motion History Questionnaire (MHQ) (Kennedy & Graybiel, 1965; Kennedy et al., 2001) and the Motion Sickness Susceptibility Questionnaire (MSSQ) (Reason, 1968; Golding, 1998). These questionnaires have been shown to predict the frequency and the severity of motion sickness symptoms over a wide range of provocative conditions, including air and sea exposure, vertical acceleration, cross-coupled Coriolis stimulation, and simulator training (Reason & Brand, 1975; Kennedy et al., 1990; Golding, 1998; Kennedy et al., 2001) and suggest a common physiological basis in the development of motion sickness (Golding, 1998; Hu et al., 1996).

Correlation coefficients between these motion sickness history questionnaires and sickness scores tend to range between r = .4 and r = .6 (e.g. Hu et al., 1996; Golding, 1998; Kennedy et al., 2001; Bos et al., 2005; Klosterhalfen et al., 2005). Overall, these studies show that the more severe the motion sickness previously experienced in different motion modes, the more severe the simulator sickness. However, the observed correlations tend to be rather low for the prediction of individual behaviours; some individuals who report no motion sickness in previous motion conditions nevertheless report substantial levels of simulator sickness. On the other hand, those individuals reporting a history of severe motion sickness generally also report the highest levels of simulator sickness. In other words, motion sickness history questionnaires are better at resolving susceptibility differences at the non- resistant end of the continuum.

In a recent study it was shown that the use of a screening procedure based on individuals’ susceptibility to other forms of motion sickness, led to a reduction in dropout rates due to simulator sickness from 20% to 5% (Reed et al., 2007). Of course, the feasibility and desirability of such screening procedures depends on factors such as available resources and acceptability of excluding individuals from participating in training. Especially with regard to the latter, it may be more desirable to expose susceptible individuals to specific familiarisation and habituation sessions.

7.3. Familiarisation

Large changes in simulator sickness have been reported between the first and the second exposure and have been considered to be a matter of familiarisation concerning the equipment and procedure rather than habituation (e.g., Regan & Price, 1993; Cobb et al., 1999; Watson, 2000). In a recent large scale driving simulator study (n=256), the mean total SSQ score for first time users was nearly 1.5 times as high as that for participants who had previous experience with the simulator; in addition, all participants who dropped out due to simulator sickness were first time users (Diels & Parkes, submitted).

Regan (1995) noted that some participants report anxiety prior to their first session and it is possible that this anxiety could manifest itself in the form of reported simulator sickness. In fact, feelings of

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anxiety are typically associated with signs and symptoms similar to those of simulator sickness and include dizziness, pounding heart, sweating or breathing difficulties (Redfern et al., 2001). Although the exact link between simulator sickness and anxiety remains elusive, anxiety has repeatedly been shown to correlate with simulator sickness (Bertin et al., 2004, 2005; Owen et al., 1998; Redfern et al., 2001). It has been suggested that anxiety may indirectly contribute to motion sickness susceptibility in that it modifies susceptibility (Reason & Brand, 1975).

Interestingly, it has been shown that actual exposure or experience with the simulator (or VR setup) is not required. Reed et al. (2007) showed that professional drivers who underwent an enhanced familiarisation process reported significantly lower sickness scores and were significantly less likely to drop out the training programme due to the occurrence of simulator sickness. The familiarisation process consisted of three to four drivers attending the simulator facility, typically 1-2 days before their scheduled simulator training sessions, and being given more information about the purpose of the simulator training programme by one of the driving instructors. Trainees were able to ask any questions they had regarding training and/or the simulators themselves and had the opportunity to the simulator in action. These findings show that even familiarisation procedures without actual experience with the simulator can be an effective tool in the management of simulator sickness.

7.4. Habituation

In the introduction (see Section 1) it was already mentioned that repeated exposure to a provocative environment renders most individuals symptom-free. Consequently, habituation has often been considered the most promising approach to the problem of simulator sickness (e.g. Kennedy et al., 1990; Watson, 2000). It is known that sickness consistently decreases over repeated simulator exposures. It has also generally been assumed that long-term habituation only occurs when exposures are less than one week apart (Bagshaw & Stott, 1985). Stern et al. (1989) reported that participants exposed to vection induced by a rotating drum did not show ‘adaptation’ with intersession intervals of 4 – 24 days, but did do so when the interval was 2 days. However, Howarth and Hodder (2008) repeatedly exposed participants to a provocative visual stimulus displayed via an HMD with different time intervals between sessions ranging from one to seven days. Unlike previous findings, the habituation which occurred was of a similar nature in all of the participant groups regardless of exposure interval and it was concluded that the number of exposures rather than the time interval between them was a more important factor. The degree of habituation was however not uniform across participants, indicating inter-individual differences in rate of habituation.

However, using a driving simulator, Watson (2000) found an optimum inter-session interval between 48 and 72 hours. Individuals with 2, 3, and 18 days between simulator trials experienced a significant reduction in sickness, while individuals with one day between exposures experienced an increase in sickness. Watson concluded that a time interval of 48 to 72 hours between simulator trials was the most effective in reducing both immediate and prolonged simulator sickness, with 48 hours being optimal for reducing the number of no-show participants.

It was already mentioned that a linear relationship exists between exposure duration and simulator sickness. Considering the occurrence of habituation, it follows that short, repeated exposures may

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provide an effective strategy to manage simulator sickness (see also McCauley & Sharkey, 1992). However, future research is required to determine how short “short” sessions have to be, whether duration exposure can increase with repeated exposures, and whether these variables in turn affect the optimum inter-session intervals. Kennedy et al. (2000) further pointed out that “…while sickness may be managed by such techniques, do other aftereffects occur such as disturbance in balance or felt limb position? And if so, how should this be managed?” (p. 469).

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8. MITIGATION GUIDELINES Successful management of simulator sickness requires an integrated approach in which known causative factors are considered in the context of the intended simulator use. System optimisation, careful selection of driving scenarios and tasks depending on individuals’ susceptibility, and effective subject handling, should all be taken into consideration, along with proactive screening of all potential participants regarding their potential susceptibility. Kennedy et al. (1990) have provided an extensive list of recommendations for alleviating simulator sickness. Since their advice was generally intended for military applications of flight simulators, Ward and Parkes (1996) subsequently tailored a list to the context of driving simulators. The below list provides an extension to this list incorporating recommendations based on new findings and experiences.

1. Participants should be briefed that symptoms are not abnormal to relieve anxiety, particularly for those not accustomed to simulation [this could be done via the Informed Consent process].

2. Persons who are experts at a task in the real environment, particularly those unaccustomed to simulation of that task, are most at risk. Such individuals have stronger expectations about sensory associations that may be foiled by simulation, and may well have no adaptation to sensory associations within the simulated environment.

3. Simulator sickness may be “contagious”. In the case of simulations with both driver and passengers or a group of participants attending the facilities simultaneously, distress evident by one person may increase symptoms or willingness to express symptoms in others. Persons in distress should be excluded or isolated from other participants at first signs.

4. Only use fit, healthy well-rested participants. Illness, fatigue, emotional upset and hangovers may make persons more susceptible and exacerbate the effects of simulator sickness.

5. Habituation to the simulation can lead to resistance to symptoms. Initial familiarization session and subsequent sessions of increasing duration for highly susceptible participants will reduce the likelihood of participants suffering from simulator sickness.

6. Simulator sessions should not be followed by substantial driving or tasks that require fine visuo-motor control such as machine handling. This is to avoid any danger of interference from aftereffects with driving and other tasks in the real world.

7. Sessions should be less than two hours to limit accumulation of symptoms, particularly fatigue related (e.g. eye strain, headache, dizziness) and aftereffects. In highly susceptible individuals, it is recommended where possible to use short (approx. 5 minute) driving sessions interspersed with breaks.

8. Include rest breaks at logical points in complex simulation tasks.

9. The participant should enter the simulator area as set up for the particular trial with the visual environment he/she is about to enter turned on to avoid participants being surprised when the visuals are turned on.

10. Driving scenarios requiring high rates of linear or rotational acceleration should be avoided or kept brief (< 5 minutes) until full habituation has been achieved. Such scenarios are most conducive to visual- vestibular conflicts.

11. Avoid freezing of the simulated scene during trials, unless preceded by the opportunity for the driver to achieve stable and straight control of the vehicle.

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12. Do not reposition the participant within the virtual world whilst simulation is active. This may lead to scene distortion.

13. Use as narrow a field of view as required by task or as appropriate by mode. Reduce the field of view in simulations that are known to be particularly provocative.

14. Discourage unnecessary head movements, particularly when first demonstrating dynamic simulations.

15. Be aware that the initial session and certain types of scenario (e.g. high scene complexity, accelerations/decelerations, cornering) require particular care.

16. All projected channels of the visual imagery should be aligned correctly and well-focused.

17. Time delays leading to cue mismatches should be avoided by minimising transport delays and frequency-dependent phase lags or leads.

18. Some activity after a simulation session requiring visual-motor co-ordination (e.g. table tennis) can facilitate readaptation to the real environment.

19. Oscillating physical motion in the vertical direction in the frequency band of 0.-0.4 Hz should be avoided or minimised [although within this project, it is recognised that this might be difficult when using the pedestrian cockpit and needs to be taken into account].

20. Motion and force cueing systems with sufficient bandwidth and minimal acceleration noise should be employed, together with appropriate wash-out algorithms to eliminate false motion cues.

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9. MITIGATION AND RECOVERY PROTOCOL The SIMUSAFE project team recognizes that simulator sickness is a possibility for participants. The research protocol has been designed to screen participants for propensity and/or past experience of motion and simulator sickness. Recruitment will be strictly targeted to exclude potential participants who:

 Have a history of high blood pressure and/or epilepsy.  Have any health conditions that may worsen under physical and/or mental stress.  Have any health conditions that may prevent adequately hearing or seeing the virtual environment.  Have a history of severe motion sickness and/or simulator sickness.

Each Cycle 3 partner may have additional exclusion criteria specific to their altered driving condition. For instance, all bicycle simulator participants must be between 150 to 180 cm tall and under 100kg.

Cycle 3 simulator tests have been set up to include many of the mitigation strategies discussed previously in Section 8 to minimise the potential for simulator sickness. Given the general likelihood of simulator sickness however, the project team will use all reasonable strategies to minimise sim sickness and the dropout rate. The following steps will be taken by all Cycle 3 partners to minimize the possibility of simulator sickness:

 All partners will provide adaptation training for participants. There will be an acclimation period where participants will be gradually introduced to the handling of the appropriate simulator.

 Airflow will be enhanced in the simulator rooms. Fans will be set up around the rooms so that air blows on the participants. The airflow will help decrease the onset of sim sickness as well as enhance the simulator experience.

 Water will be available at each cockpit as well as in the space dedicated for participant recovery.

 Multiple breaks have been built into the research design. During breaks, researchers will encourage participants to walk around and get fresh air.

 Participants will be given a pair of travel-sickness wristbands to wear while using the simulators. There is some indication these may mitigate motion sickness.

 Participants will be asked at least 3 times during the study (before, during, and after) about their perceived levels of simulator sickness (SSQ). To better monitor the development and recovery of simulator sickness, partners will take baseline measurements with the SSQ.

 A spillage kit must be kept near the simulators but out of sight of participants. If a participant shows symptoms of simulator sickness, the researchers will stop his or her simulator use immediately. As simulator sickness can be contagious, the sick participant will be helped to leave the simulator and the room. They will be escorted to designated recovery area and

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asked to sit with their feet up, or lie down1. The researchers will request the sick participant rest with his or her eyes closed for at least 5 minutes. Water will also be offered. Motion sickness bags will be kept nearby but out of sight of the participants. After several minutes of recovery, the SSQ will be administered again to check on the participant’s level of recovery. This will be repeated every 10 or 20 minutes until the baseline scores are repeated. If the participant has not attained pre-simulator sickness baseline levels but says they are fine to leave, the research team will ask them to sign a Caution Acknowledgement Waiver before they depart:

We suspect you are experiencing simulator sickness. For your safety, we strongly encourage you to take steps to alleviate some of the symptoms you now feel. You may feel good enough to leave now, but the symptom relapse are possible. Most probably, the symptoms will decrease in a short period (generally less than one hour, rarely over two hours).

We do not want you to drive until we are confident you have recovered. We recommend [a short test] to monitor your recovery. [It] will be administered periodically until you recover. If you still want to leave without us fully monitoring your recovery, we request you sign this release form to acknowledge we recommend you not leave the [research lab] until you have recovered. (Hoffman et al. 2003). If a participant has not regained baseline levels within two hours and/or needs to leave, partners will arrange another way to get them back to work or home, such as via a taxi or friend. All participants who terminate due to sim sickness will be contacted within 24 hours to follow up on their recovery. Upon completion of the simulation ride/drive, whether participants completed the course or ended early due to discomfort, participants will be asked to complete the final questionnaires. This will ensure participants will have several minutes to recover after their simulator use in case of any residual dizziness, etc.

1 Simulator sickness recovery protocol is adapted from Hoffman et al. (2003).

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