# Modelling individual motion sickness accumulation in vehicles and driving
simulators

Varun Kotian${}^{\text{1}}$, Daan M. Pool${}^{\text{2}}$ and Riender
Happee${}^{\text{1}}$ (

###### Abstract

Users of automated vehicles will move away from being drivers to passengers,
preferably engaged in other activities such as reading or using laptops and
smartphones, which will strongly increase susceptibility to motion sickness.
Similarly, in driving simulators, the presented visual motion with scaled or
even without any physical motion causes an illusion of passive motion,
creating a conflict between perceived and expected motion, and eliciting
motion sickness. Given the very large differences in sickness susceptibility
between individuals, we need to consider sickness at an individual level. This
paper combines a group-averaged sensory conflict model (as in [17]) with an
individualized accumulation model (as in [14, 9, 7]) to capture individual
differences in motion sickness susceptibility across various vision
conditions. This consideration of the effect of vision is crucial in driving
simulators where there is a strong contribution of visual cues. The model
framework can be used to develop personalized models for users of automated
vehicles and improve the design of new motion cueing algorithms for
simulators. The feasibility and accuracy of this model framework are verified
using two existing datasets with sickening conditions in 1) an experimental
vehicle with and without outside vision [9], and 2) comparing vehicle
experiments with corresponding driving simulator experiments [16]. Both
datasets involve passive motion, representative of being driven by an
automated vehicle. The model is able to fit an individual’s motion sickness
responses using only 2 parameters (gain $K_{1}$ and time constant $T_{1}$), as
opposed to the 5 parameters in the original model. This ensures unique
parameters for each individual. Better fits, on average by a factor of 1.7
(for Accum_2 model), of an individual’s motion sickness levels, are achieved
as compared to using only the group-averaged model (Accum_0 model).
Furthermore, this model framework demonstrates robustness by accurately
modeling various datasets with distinct motion and vision conditions. Thus, we
find that models predicting group-averaged sickness incidence cannot be used
to predict sickness at an individual level. On the other hand, the proposed
combined model approach predicts individual motion sickness levels and thus
can be used to control sickness.

###### keywords:

Motion Sickness, Simulator Sickness, Modeling, Driving Simulators, Automated
Vehicles.

1) Delft University of Technology, Faculty of Mechanical, Maritime and
Materials Engineering, Cognitive Robotics, 2628 CD Delft, e-mail:
<EMAIL_ADDRESS>
(2) Delft University of Technology, Faculty of Aerospace Engineering, section
Control & Simulation, 2629 HS Delft, email<EMAIL_ADDRESS>
## Introduction

Automated vehicles and driving simulators are very different technologies.
However, they both share two common facts. The first is that they have become
very popular in recent years, a trend that is expected to continue in the
future. Secondly, they both share a common issue in motion sickness. Users of
automated vehicles will move away from being drivers to passengers, preferably
engaged in other activities such as reading or using laptops and smartphones,
which strongly increases susceptibility to motion sickness. Similarly, in
driving simulators, the apparent visual motion combined with scaled or without
any physical motion causes an illusion of passive motion, creating a conflict
between perceived and expected motion, and eliciting motion sickness [3].
Though these two cases seem different, the inherent mechanism that causes
motion sickness in both, i.e., sensory conflict, is the same.

The mechanisms behind the development and evolution of motion sickness have
been studied extensively, relying heavily on models that predict sensory
conflicts based on inputs from the vestibular and visual sensory systems [2,
17, 13, 8, 12]. These models are known as ‘conflict generation’ models.
However, these models predict group-averaged sickness responses (conflicts),
using Motion Sickness Incidence (MSI), and thus cannot be reliably used for
predicting and controlling sickness, both in real vehicles and simulators, at
an individual level.

Figure 1: Framework for combined ‘conflict generation’ [17, 13] and ‘conflict
accumulation’ [14, 7] models to predict individual motion sickness, i.e.,
MIsery SCale [15]

‘Conflict generation’ models, such as the SVC model by [17], calculate MSI
using a conflict accumulator that integrates the generated conflicts in time.
However, this is a single low-pass integrator and hence is not able to model
the fast dynamics of motion sickness such as recovery. One solution is to make
use of the same ‘conflict generation’ model and replace the accumulation
integrator with a much more complex integrator model (as in [14]), which will
be referred to as the ‘conflict accumulation’ model in this paper (see fig.
1). These ‘conflict accumulation’ models have been found very useful in motion
sickness predictions for one degree of freedom motion [9, 7]. These will be
personalized to each individual with a unique set of parameters. Further, a
complex ‘conflict accumulation’ model will be able to capture phenomena like
recovery from motion sickness and hypersensitivity [9, 7]. The personalization
of parameters has already been shown to improve modeling accuracy by [9] where
it increased the accuracy by a factor of 2 as compared to group-averaged
modeling. Our work extends this to 6 degrees of freedom (DOF). This model has
the additional benefit of allowing visual inputs. This is important when
predicting motion sickness in simulators, i.e. simulator sickness, where there
is a strong influence of vision. Additionally, individual motion sickness
metrics, such as those obtained using the MISC scale [5], instead of group-
averaged metrics like Motion Sickness Incidence (MSI) (as in [17, 12]), allow
individualized fitting of the model resulting in better prediction in future
motion paradigms for the individual. Hence, in this paper, we aim to combine
the ‘conflict generation’ model with a ‘conflict accumulation’ model to
improve the ability to capture the differences in individual susceptibility.
This model framework is visualized in fig. 1.

## Research Question

The goal of the paper is to verify the feasibility of the combined motion
sickness model approach, i.e., combining ‘conflict generation’ and ‘conflict
accumulation’ models, to capture individual differences in motion sickness
susceptibility across various vision conditions. To do this we have two
further sub-goals:

  * •

To verify the accuracy of the combined conflict generation/accumulation
modeling approach

  * •

To find a balance between model accuracy and the number of estimated
accumulation model parameters

The second goal ensures a practical model with estimated parameter sets unique
to each individual. We use two existing datasets with sickening conditions in
1) an experimental vehicle with and without outside vision [9], and 2)
comparing vehicle experiments with corresponding driving simulator experiments
[16]. Both datasets involve passive motion, representative of being driven by
an automated vehicle.

By achieving this goal, this model framework can be used to develop
personalized models for users of automated vehicles, which the automated
vehicle can use to determine the comfort levels of the user and subsequently
best adapt its driving style, i.e., by limiting the acceleration and rotations
of the car. This model framework can also improve and accelerate the design of
new optimization-based motion cueing algorithms that aim to minimize sickening
motions in simulators like in [6, 1]. Here, the tilting to replicate specific
forces and/or visual cueing delays will be optimized, which will result in
fewer dropouts of participants due to motion sickness.

## Methodology

Table 1: Experimental datasets used in this study

Datasets | Details | Reference | No. of Participants
---|---|---|---
| Slalom Drive
---
Dataset
| Slalom with Internal and External vision
---
Motion sickness responses for hypersensitivity
[9] | 16
| Car and Simulator
---
Dataset
| Naturalistic drive in vehicle and moving base simulator
---
Only External vision
[16] | 24

### Experimental Datasets

For the analysis in this paper, we make use of an existing real-world ‘Slalom
Drive’ dataset (16 participants) with varying vision conditions, i.e., with
and without an outside view, where mean MISC levels at the end of motion
exposure were 5.3 (severe symptoms) with an outside view and 3.3 (some
symptoms) without an outside view (Experiment 1 in [9]), see table 1 and fig.
3. This experiment compared the motion sickness development with and without
an outside view from the car. This dataset is ideal for proving that our new
model framework can simulate various vision conditions in actual vehicles. The
model framework is also validated using the ‘Car and Simulator’ dataset (24
participants) by [16]. This experiment contains motion sickness responses from
real-world driving and its (matched) simulation on a moving-base driving
simulator [16]. For this experiment, the mean MISC levels at the end of motion
exposure were around 5.5 (severe symptoms) in the car and 1.5 (slight
discomfort or vague symptoms) in the simulator (see table 1 and fig. 3). Using
the Car and Simulator dataset, we demonstrate the capability of the model
framework to generalize well for different types of passive motion, whether it
originates from a car or a simulator. Furthermore, both datasets are used to
study the number of parameters needed to accurately simulate the motion
sickness development in different participants.

Both datasets capture sickness responses to passive motion, representative of
being driven by an automated vehicle as well as driving simulators. In these
datasets individual motion sickness levels (using MISC) were reported as a
function of time with motion stimuli varying in time, showing major individual
differences in sickness susceptibility, making this data suitable to model
sickness accumulation.

Figure 2: Slalom Drive dataset [9] group-averaged MISC levels versus time in
the conditions of external (blue) and internal vision (red)

Figure 3: Car and Simulator dataset [16] group-averaged MISC levels versus
time in the car (blue) and simulator (red)

### Inputs and Outputs

Inertial inputs (on the left side of fig. 1), such as acceleration and angular
velocity, and vision inputs (on the left side of fig. 1), such as visual
verticality (orientation) and visual rotation (rotational velocity), will be
input into this model framework. The outputs (MISC) will be compared to the
ground truth from the experiments and the model parameters (time constants and
gains, explained in detail in the next section) in the ‘conflict accumulation’
model will be tuned to have the best fit to individual responses. More details
about each model are given in the following subsections. We applied 6D motion
(3D translation, and 3D rotation), using the recorded seat and platform motion
for the Car and Simulator dataset, and recorded head motion for the Slalom
Drive dataset.

The human eye estimates motion through vision by measuring the rotation of
visual cues between the current and previous states, which is known as optic
flow. Therefore, we made the assumption that the visually perceived rotations
would be equivalent to head (or vehicle, as in the case of the Car and
Simulator dataset) rotations when observing the external environment. For
internal vision, we select a zero visual input assuming no head motion
relative to the vehicle.

### Selection of models

As previously discussed, we combine two models in our proposed model
framework. The first component of the model is the ‘conflict generation’ model
(see the left part in fig. 1), which models the integration of sensory inputs
and generates a one-dimensional sensory conflict signal. This model should be
reliable in forecasting conflict signals consistently, especially while
driving around in a vehicle. Additionally, the model must be capable of
incorporating visual inputs so that the effect of different vision conditions
can be studied. To select an appropriate model, we refer to a study by [12]
where various models and implementations of vision inputs were compared. Based
on their findings, we conclude that the 6DOF Subjective Vertical Conflict
(SVC) model with Visual Rotational Velocity input [17] is ideal for our
purpose of predicting motion sickness. This model has been shown to accurately
replicate the frequency and amplitude dynamics of motion sickness as reported
in numerous literature studies. It is shown that the Visual Vertical (red
pathways in fig. 1) input as implemented in the SVC model was not effective in
predicting sickness and hence will not be used in the current study (and set
the corresponding gains to zero).

The second part of the model is the ‘conflict accumulation’ model. As the name
suggests, this model accumulates (integrates) the sensory conflicts generated
by the first part of the model framework to estimate the level of motion
sickness experienced by an individual as it builds up over time. We adopt the
more advanced Oman model [14] for this. The Oman model is a five-parameter
non-linear model that integrates the conflict (see the right part in fig. 1),
with fast and slow pathways combined with leakage. The fast path has a very
low time constant ($T_{1}$) and models the direct response to a sickening
stimulus. The slow path has a high time constant ($T_{2}$) and captures the
long-term effects of sickening stimuli, such as recovery and hypersensitivity.
This is also relevant in simulators where a sudden increase in motion
incongruence can make participants hypersensitive. This has been observed by
[4, 11], where it was shown that motion tends to be momentarily bad, but not
continuously. Both pathways have a gain ($K_{1}$ and $K_{2}$) to control the
contribution of each path. Additionally, there is a power law ($pow$) at the
model’s output to account for nonlinear scaling effects.

In this paper, we use the combined model to predict sickness as rated with the
MIsery SCale (MISC) [15, 5]. The MISC is an 11-point symptom-based scale
(0-10) that is used to query motion sickness in various studies (such as in
[9, 7, 16]).

### Parameter reduction in the Accumulation model

In addition to studying the accuracy of the Accumulation model as it is (with
5 individual parameters), we also tried to reduce the number of parameters
needed while ensuring goodness of fit. This will prevent over-fitting and
result in unique solutions. To do this we use empirical relations and median
values of parameters as observed in previous studies and current simulations
with various numbers of parameters.

  * •

$K_{2}=5K_{1}$; from [14]

  * •

$T_{2}=7T_{1}$; from [9]

  * •

$pow=0.4$; from [7]

  * •

$T_{1}=60$ s; from [14]

  * •

$K_{1}$ best fit within datasets (median values from individual estimates)

    * –

$K_{1}=2$; for Slalom Drive dataset

    * –

$K_{1}=18$; for Car and Simulator dataset

We use combinations of these assumptions to test if the number of parameters
can be reduced. Table 2 summarizes the different cases of the accumulation
(Oman) model with the parameters that are estimated (marked with a ✓) and the
parameters that are held constant or as an empirical relation to the estimated
parameters. $K_{1}$ and $K_{2}$ are the gains, $T_{1}$ and $T_{2}$ are the
time constants and $pow$ is the power term in the accumulation model in fig.
1. Accum_5 is the original version of the accumulation model from [14].
Accum_0 is the model with group-averaged parameters. The $K_{1}$ is different
for each dataset as the motion captured in each dataset is different. In the
‘Slalom Drive’ head motion was recorded, however, in the ‘Car and Simulator’
only vehicle motion was recorded. Hence, there is a difference in the
magnitude of rotations which explains the difference in estimated gains
($K_{1}$).

Table 2: Parameter reduction. Simplified versions of the ‘conflict accumulation’ model with a different number of parameters. Check marks indicate that the parameter is estimated for each individual. Label | # parameters | $K_{1}$ | $K_{2}$ | $T_{1}$ | $T_{2}$ | $pow$
---|---|---|---|---|---|---
Accum_5 | 5 | ✓ | ✓ | ✓ | ✓ | ✓
Accum_4a | 4 | ✓ | $5K_{1}$ | ✓ | ✓ | ✓
Accum_4b | 4 | ✓ | ✓ | ✓ | $7T_{1}$ | ✓
Accum_3 | 3 | ✓ | $5K_{1}$ | ✓ | $7T_{1}$ | ✓
Accum_2 | 2 | ✓ | $5K_{1}$ | ✓ | $7T_{1}$ | 0.4
Accum_1a | 1 | 2 or 18 | $5K_{1}$ | 60 | ✓ | 0.4
Accum_1b | 1 | 2 or 18 | ✓ | 60 | $7T_{1}$ | 0.4
Accum_0 | 0 | 2 or 18 | $5K_{1}$ | 60 | $7T_{1}$ | 0.4

### Parameter estimation in the accumulation model

To allow the ‘conflict accumulation’ model to accurately capture the
individual differences in motion sickness susceptibility, the parameters need
to be set for each individual. Further, to model both conditions together,
only one set of parameters will be estimated for both conditions for each
individual. To fit these parameters a constrained optimization problem is
formed. This estimation is carried out in MATLAB with the fmincon solver using
the sqp algorithm. In addition to this, multistart was used to simultaneously
find 16 local minima and then find the lowest out of them. This way it is
ensured that we find the global minimum. The Root-Mean-Squared Error (RMSE)
between the actual and predicted MISC responses, as a function of the
parameter vector $x$, is chosen as the cost function for the estimation. As
both conditions are fitted together to give one set of parameters, the cost
function is the sum of RMSE for both conditions. The optimization problem
along with the constraints are shown in eq. 1.

$\begin{array}[]{cc}\underset{x}{\text{minimize}}&RMSE_{C_{1}}(\boldsymbol{x})+RMSE_{C_{2}}(\boldsymbol{x}),\\\
\text{where},&x=(K_{1},K_{2},T_{1},T_{2},pow)^{T}\end{array}$ (1)

## Results

Figure 4: Slalom Drive vehicle tests. Motion sickness responses (MISC) in
experiments by [9] in black, fitted Accum_2 model predictions (MISC) in green,
fitted Accum_5 model predictions (MISC) in dashed violet, fitted Accum_0 model
predictions (MISC) in dotted grey, and MSI predictions from Hill function in
orange for 4 participants (participant label shown on the left) for the
conditions of external (right column) and internal (left column) vision.

In this paper, we propose a new model framework that combines the ‘conflict
generation’ model with an advanced ‘conflict accumulation’ model to improve
the ability to capture differences in individual susceptibility. Using the
methods described in the previous section, simulations were run and the
results are presented below. These results are presented with the aim to
justify our goal, i.e, to verify the feasibility and the accuracy of the new
model framework. It is hypothesized that using a ‘conflict accumulation’ model
that uses individual parameters will be more accurate than a ‘conflict
accumulation’ model that uses group-averaged parameters, as also reported by
[9]. First, results are shown for the Slalom Drive dataset by [9] to show the
performance of the model with varying vision conditions. This is followed by
results for the Car and Simulator dataset by [16] where the adaptability of
the model to real-world driving and driving simulators is shown. Fits of the
model framework to the actual MISC responses with Motion Sickness Incidence
(MSI) predictions overlayed are shown first followed by their RMSE with the
actual MISC responses.

Figure 4 show the comparison of the various models with experimental recorded
motion sickness responses (MISC). It is evident that our approach of
estimating parameters for each individual (in particular for Accum_2 and
Accum_5 models) offers improved accuracy in predicting MISC responses compared
to the use of group-averaged parameters (Accum_0). The average RMSE reduces
from 1.13 and 0.74, for Accum_2 and Accum_5 models, to 1.94, for the Accum_0
model. This proves that using parameters estimated for each individual
(Accum_2 and Accum_5) is, on average, 1.7 times better than using group-
averaged parameters, as in the Accum_0 model.

Another important observation is that all the accumulation models capture the
recovery from motion sickness. This recovery occurs when the sickening stimuli
are stopped and the participant is allowed to rest. This is more evident in
participants 9 and 14 (second and third row). The Hill function predicting MSI
(in orange) is not able to capture this reduction of motion sickness. MSI
reports the percentage of people who might get motion sick but does not give
information about any certain individual.

Figure 5: Root Mean Squared Error (RMSE) between the predicted MISC and the
actual MISC for the Slalom Drive dataset. Shown are the mean (circle), median
(horizontal solid line), and interquartile range (colored rectangle)

Figure 7: Car and Simulator tests. Motion sickness responses (MISC) in
experiments by [16] in black, fitted Accum_2 model predictions (MISC) in
green, fitted Accum_2 model predictions (MISC) in dashed violet, fitted
Accum_0 model predictions (MISC) in dotted grey, and MSI predictions from Hill
function in orange for 4 participants (participant label shown on the left)
for the case in the car (left column) and the simulator (right column) vision.

Additionally, we also evaluated the need for each of the parameters. We did
this by reducing the number of parameters by using relations found during the
previous simulations and also what was observed and reported in various
studies. We used 5, 4, 3, 2, 1, and 0 parameters to evaluate the need for the
parameters, see table 2. From fig. 5 it is clear that reducing the number of
parameters below 2 leads to a 36% increase in RMSE (from 1.2 to 1.6 in
internal vision case and from 1.0 to 1.4 in external vision case when
comparing Accum_2 to Accum_1a model). Hence, any model with 2 or more
parameters is sufficiently accurate to capture the motion sickness
development. We also compared the individual models with a group-averaged
version of the accumulation model (Accum_0), where the parameters are the same
for all participants in the dataset. It is observed that Accum_0 has on
average 1.7 times more RMSE (from 2.25 to 1.22 in the internal and from 1.63
to 1.04 in the external vision case) as compared to the Accum_2 model. Thus,
parameters estimated for each individual are more accurate than group-averaged
parameters.

Figure 6 shows the distribution of the two estimated parameters ($K_{1}$ and
$T_{1}$) in the Accum_2 model for the Car and Simulator dataset. The black
line shows the parameters for the Accum_0 model as a reference. It can be
observed in that there is a wide range of parameter combinations (median: 2
and 83.5, standard deviation: 3.8 and 190.5, for $K_{1}$ and $T_{1}$,
respectively) describing individuals that show variations in motion sickness
susceptibility. These parameter sets can be classified into three groups of
motion sickness susceptibility: high, medium, and low. High susceptibility are
those with large $K_{1}$ and small $T_{1}$. Low susceptibility have the
opposite, small $K_{1}$ and large $T_{1}$. Lastly, medium susceptibility have
small $K_{1}$ and small $T_{1}$. With this knowledge, representative
parameters can be generated to test motion profiles on different motion
sickness susceptibilities. Additionally, percentiles can be defined to see
which percentile of subjects do or do not get motion sick.

Figure 6: Slalom drive dataset parameter distribution (estimated gain
($K_{1}$) and time constant ($T_{1}$)) for the Accum_2 model (blue) and
Accum_0 model (black)

Furthermore, we validated these models on the car and simulator dataset by
[16]. This dataset had 24 participants, each experiencing motion with external
vision in a real-world car and in a simulator. Figure 7 shows the experimental
recorded motion sickness responses (MISC) in black, MSI predictions in orange,
and fitted model predictions in green, violet, and grey for 4 out of the 24
participants for both cases. It can be seen that even with 2 parameters, the
fits of the Accum_2 model are very close to the actual MISC responses in both
the car (RMSE mean: 1.03, std:0.544) and simulator (RMSE mean:1.06, std:
0.605). The MSI prediction values are very low. This is due to the low levels
of conflict generated in these experiments as compared to the slalom drive by
[9]. Another reason may be that the participants sampled in [16] may be highly
susceptible to motion sickness. The ‘conflict Accumulation’ model is able to
account for these combinations of low levels of conflict and high
susceptibility of the participants adequately.

Lastly, to demonstrate that the two parameters are the most optimal number of
parameters even for this dataset, this was tested with other versions of the
model with varying numbers of parameters. As shown in Figure 8, there is a 25%
increase in RMSE values (from 1.03 to 1.38 in the case of motion sickness in
the car and from 1.06 to 1.29 in the simulator) when comparing Accum_2 to
Accum_1a. When comparing the individual models with a group-averaged version
of the accumulation model, it is observed that the group-averaged model,
Accum_0, has 1.64 times more RMSE (from 1.96 to 1.03 in the internal and from
1.47 to 1.06 in the external vision case) as compared to the Accum_2 model.
This increase is close to what was seen in the Slalom Drive dataset where a
1.7 times increase was observed. Thus, parameters estimated for each
individual are more accurate than group-averaged parameters.

Figure 8: Root Mean Squared Error (RMSE) between the predicted MISC from the
models and the actual MISC for the Car and Simulator Dataset. Also shown are
the mean (circled), median (horizontal solid line), and interquartile range
(in colored rectangle)

Figure 9 shows the distribution of the two estimated parameters ($K_{1}$ and
$T_{1}$) in the Accum_0 and Accum_2 model for the Car and Simulator dataset.
The observations are equivalent to those reported from fig. 6. Individual
variations in motion sickness susceptibility are shown by a wide range of
parameter combinations (median: 18 and 47, standard deviation: 16.1 and 221.8,
for $K_{1}$ and $T_{1}$, respectively). However, there is a difference in the
gain value ($K_{1}$), due to the different input data used for both datasets.
In the Slalom Drive dataset, head motion is used while in the Car and
Simulator dataset vehicle motion is used as the head motion was not available.
Ideally, we would like to use head motion as those motions are perceived by
the vestibular and visual systems.

Figure 9: Car and Simulator dataset parameter distribution (estimated gain
($K_{1}$) and time constant ($T_{1}$)) for the Accum_2 model (blue) and
Accum_0 model (black)

It is clear from the results in figs. 4 and 7 that this model framework, is
able to capture two different conditions with a single set of 2 parameters.
This is applicable for various vision conditions, as well as different motions
from real cars and simulators.

## Discussion

This paper introduces a novel model framework to predict an individual’s
motion sickness level in vehicles and simulators. This model framework
combines a group-average ‘conflict generation’ model with an individual
‘conflict accumulation’ model to better capture individual susceptibility
differences. This is important to understand and capture differences in motion
sickness susceptibility. Furthermore, by using a ‘conflict generation’ model
that includes visual inputs, various vision conditions (such as external,
internal, and only vision) can be simulated as well. This is crucial in
simulators where motion sickness occurs due to a strong influence of visual
cues. We hypothesized that using a ‘conflict accumulation’ model that uses
individualized parameters will result in greater accuracy compared to a model
that uses group-averaged parameters. Hence, in this paper, we assessed the
feasibility and accuracy of this new model approach for motion sickness
predictions.

It is clear from the results (see figs. 4 and 7) that the Oman model as a
‘conflict accumulation’ model with individualized parameters is able to better
model the motion sickness responses of individuals as compared to using the
group-averaged models such as the Accum_0. In addition to this, it also
captures the recovery phase of the experiment (see figs. 4 and 7) and,
theoretically, the hypersensitivity in the following second motion exposure.
This recovery phase takes a few minutes and is not captured by the Hill
function, which predicts MSI, due to its time constant of 12 min. These
results are in line with the work by [9] where individualized fits with the
Oman model reduced the prediction error by a factor of 2 in a slalom drive
with a frequency of 0.2 Hz and lateral accelerations with a peak amplitude of
0.4 g with eyes closed. Our work extends this, by using a six degrees of
freedom ‘conflict generation’ model to generate the conflict, in realistic
driving conditions with varying vision conditions. This way a single set of
parameters, estimated using data from all required conditions, can
characterize an individual across various motion and vision conditions.

We also looked into reducing the number of parameters in the model. We found
that using the relations and values mentioned in previous studies (such as in
[14, 9, 7]), the number of individual parameters can be limited to 2. This way
we can be sure that the parameter set estimated is unique for each individual
and is not an over-fit on the available sparse (in conditions) dataset.
Another by-product is that the computation time required for the estimation of
parameters is significantly reduced (by a factor of 4, from 48 to 11 seconds
for 40 min of simulation). However, even with 5 parameters the model can be
used for real-time applications.

Using these models, the motion profiles in simulator experiments can be tuned
to reduce dropout of participants in simulator experiments due to motion
sickness. Not only is the model useful in designing experiments but also can
be employed in motion cueing algorithms in driving simulators, as well as in
automated vehicles. Using a model-based control method, this model framework
can be included in the plant model to forecast motion sickness levels, as in
[10]. This way, motion sickness levels can be controlled by taking into
account each individual’s susceptibility. Vehicle motion in automated vehicles
and platform motion and tilt coordination in simulators can be optimized to
minimize their effect in eliciting motion sickness. Thus, this model is
feasible to use both post-experiment and in real-time during the experiment.

We now tuned individual parameters of the accumulation model while keeping the
many parameters of the sensory conflict model constant. A main limitation of
the model is that it fails to accurately fit the subject’s responses who get
highly motion sick during internal vision and do not get sick during the
external vision case (for example P14 in fig. 4). This sharp shift in motion
sickness dynamics cannot be captured by our model framework. To do this, at
least one additional parameter needs to be estimated, which could be the
vision gain in the ‘conflict generation’ model or other perception parameters.
People not only differ in motion sickness susceptibility, but also in their
perception of motion. This has been shown in [8], where a correlation
coefficient of 0.74 was observed between individuals’ overall sickness
sensitivity and their subjective vertical time constant. Thus each human will
have a different contribution of visual and vestibular signals for their state
estimation. By adjusting the parameters in the ‘conflict generation’ model,
the contribution of vision to the estimates can be tuned.

Another limitation is that the model gain, $K_{1}$, needs to be adapted for
the two datasets, and presumably depending on the location of the IMU (head or
seat). This can be seen in the difference in the range of estimated gain
($K_{1}$) values in the two datasets (see figs. 6 and 9). Also, the Hill
function accumulation model predicted a reasonable MSI magnitude in the Slalom
Drive but highly underestimated sickness in the Car and Simulator dataset.
Ideally, we would like to always use recorded head motion, which is more
representative of the motion experienced by the vestibular system. If this is
not available we could use biomechanical human/seat models or linear transfer
functions to convert 6D vehicle motion to 6D head motion. This way it can be
ensured that the estimated parameters can be used for any case.

## Conclusion

A new model framework was developed to fit and predict individual motion
sickness levels in vehicles and simulators. This model framework contained two
parts: ‘conflict generation’ and ‘conflict accumulation’. The model utilizes
acceleration and angular rotational data as inputs and adjusts the parameters
of the ‘conflict accumulation’ model to fit each individual’s motion sickness
level (reported using MIsery SCale (MISC)) response. This framework is able to
fit individual responses with just two estimated parameters (a gain $K_{1}$
and time constant $T_{1}$) with an average RMSE of 1.1 MISC. By combing the
two models, better fits, on average by a factor of 1.7 (for Accum_2 model), of
an individual’s motion sickness levels are achieved as compared to using only
the group-averaged models. We fit both conditions (External and Internal
vision conditions for the Slalom Drive dataset and driving in a car and a
simulator in the Car and simulator dataset) together to estimate one set of
parameters. The model is able to simulate both conditions using a single set
of estimated parameters for each participant. This framework has the potential
to show the impact of vision on an individual level, offering a more
personalized approach compared to previous models. This is important
especially in driving simulators, as the effect of delays in visual cues, can
be quantified with this model. This model framework also shows robustness by
accurately modeling different datasets with completely different motion and
vision conditions.

These individual models can be used in further studies for predicting motion
sickness levels of the same individual in a different motion scenario, thus
reducing the dependence on extensive experiments on humans. Finally, this
model framework can also aid in faster testing of new motion cueing algorithms
for driving simulators due to less sickening driving simulator experiments and
may reduce the dropout of participants in driving simulator evaluations.

## Acknowledgements

We thank Tugrul Irmak (Delft University of Technology, The Netherlands) and
Tessa Talsma (Skoda Auto, Czechia) for sharing the experimental data. The
contribution of Varun Kotian was financially supported by Toyota Motor Europe.

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