"Modelling Player Preferences in AR Mobile Games
Vivek R. Warriar, John R. Woodward and Laurissa Tokarchuk
School of Electronic Engineering and Computer Science
Queen Mary University of London
London, UK
{v.r.warriar, j.woodward, <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
Abstract—In this paper, we use preference learning techniques
to model players’ emotional preferences in an AR mobile game.
This exploratory study uses player behaviour to make these
preference predictions. The described techniques successfully predict players’ frustration and challenge levels with high accuracy
while all other preferences tested (boredom, excitement and fun)
perform better than random chance. This paper describes the AR
treasure hunt game we developed, the user study conducted to
collect player preference data, analysis performed, and preference
learning techniques applied to model this data. This work is
motivated to personalize players’ experiences by using these
computational models to optimize content creation and game
balancing systems in these environments. The generality of our
technique, limitations, and usability as a tool for personalization
of AR mobile games is discussed.
Index Terms—Augmented reality, mobile games, player preference modelling, content creation, player experience, preference
learning, linear classifiers.
I. INTRODUCTION
Augmented Reality (AR) experiences have grown in popularity recently. The most popular medium for AR games are
mobile devices, possibly due to the increasing simplicity of
building and deploying mobile AR experiences. Popular AR
SDKs such as ARKit and ARCore use the camera and inertial
sensors to extract the device’s position and orientation [1]. This
information is then used to overlay digital content in the space
around the device, which can be viewed through the device.
This work focuses on AR games that involve players physical
exploration of their local space. We focus on physical AR
games for 2 reasons. First, they follow trends in mobile AR
games that are more relatable to past experiences players have
had, such as playing Pokemon GO (PoGo), Ingress1 or dARk2
.
These games use narrative and game design to engage people
with their surroundings (albeit in a more complex manner
than our study game). Second, the potential health benefits of
physical AR games make a strong case to personalize these
environments to promote healthy behaviour among players.
We use player movement, which is the time-series data of the
device’s position and rotation during the game. In turn, this
data is treated as player behaviour in our work.
1PoGo and Ingress are both location-based AR games developed by
Niantic
2dARk is a short story horror experience developed by Combo studio
Following similar studies in player modelling [2], [3], we
use player behaviour features (PBFs) and controllable game
features (CGFs) to predict a player’s emotional preferences.
For example, does the player find level A more fun than
level B or vice versa? Effective prediction of such preferences
will enable procedural content generation (PCG) or game
balancing systems to be optimized to a player’s ideal emotional
preferences. Our approach is novel, in that it uses movement
data to model players’ preferences. Ground truth is established
through data collected from self-reported questionnaires. Exploring this domain in the context of subjective preferences is
useful for personalizing game experiences.
Since data from popular AR mobile games is unavailable,
we developed an AR Treasure-hunt game that is similar to
existing games by incorporating reward systems, exploration
of local space, and a narrative that motivates these rewards
(described in section III). Before introducing the game, a
background of relevant work is provided in the next section.
We conducted a user study to collect player behaviour and
preference data, described in section IV. We then perform
inferential statistics and machine learning on PBFs and CGFs
in order to understand the relationship between these features
and players’ preferences (methodological details are described
in section V and the results from the analysis are described in
section VI). As shown in [2], [3], preference models can be
learned using different computational methods. In this paper,
we show that a combination of CGFs and PBFs can be used
to accurately predict a number of dimensions of emotional
preferences. For AR mobile games, we propose a different
model obtained with Support Vector Machines (SVM) which
shows better performance than others tested. We also make
feature recommendations from the set of PBFs and CGFs
tested. Finally, the implications of these results, limitations,
and future work are discussed in section VII.
II. BACKGROUND
Research in AR games has explored techniques to enhance
PX, through PCG [4] and automatic game balancing [5], [6].
Despite this, we have observed a lack of studies that model PX
in AR. Exploring this area will complement existing research
to enhance personalization. Theoretical frameworks such as
Experience-Driven Procedural Content Generation [7] offer
directions to personalize game content. These approaches are
driven by computational models of PX. This has been suc978-1-7281-1884-0/19/$31.00 ©2019 IEEE
View metadata, citation and similar papers at core.ac.uk brought to you by CORE
provided by Queen Mary Research Online
Fig. 1. Fig. [a-g] show the flow of a single round of the game. [a]: Shows the screen to select an experiment session. [b]: The options for a user to place a
game map. [c]: The (green) start button that the player must tap in order to begin the game. [d]: The bubbles in the game indicating treasure in close by. [e]:
Treasure that appears which the player collects. [f]: The 2D puzzle presented to the player in the unsolved form. The white squares indicate treasure pieces
that were not collected. This screen is presented to the player once the exit area is entered (seen in blue in fig [b] and [d]). [g]: The solved 2D puzzle.
cessfully applied to traditional digital games [2] and physical
interfaces [5]. AR mobile experiences are ‘hand-held video
see-through experiences’ [1] that offer a variety of sensor data
such as time, location, and movement. This is a more complex
environment for the creation of computational models of PX.
We use player movement to predict preferences. Related work
has been conducted in [8] and [9] in detecting emotions
from movement data of people playing Nintendo Wii games.
In [9], a different approach to predicting affective states3
is followed. Our approach is similar to [8], which predicts
distinct emotional states (Triumph, Concentration, Defeat and
Frustration were the states explored in the study). The 2 studies
are similar, as ground truth for predictions is multiple observer
agreement. Our approach differs: ground truth is established
from self-reported questionnaire data from players.
III. AR TREASURE-HUNT: TEST-BED PLATFORM GAME
The aim of the game is to collect all hidden treasure from a
constrained space. This treasure has been randomly distributed
within the level area. The treasure pieces are parts of a picture.
In order to win, the player will have to put together this picture
(like a 2D puzzle). Figure 1 shows the screens during a single
round of the game. Levels of the game vary across the number
of treasure pieces and the size of the game area. This game
can be played in parks and other open spaces.
The player places the AR level in the world before starting
the game. The game begins once the player finalizes this
placement. While a player is exploring the level, the boundary
of the AR level and an exit (to the 2D puzzle) is visible to
them. They will not be aware of how many treasure pieces are
hidden in the level. These pieces are invisible to the player
by default. If a player is close to a treasure piece they will
receive an audio-visual clue. The clues are implemented using
a particle system that is designed to look like a bubble emitter
with an appropriate sound effect. Bubbles are emitted around
3Affective states is an approach to measuring and contextualizing emotions according to some dimensional space, usually: Valence and Arousal
the position of the treasure piece. Treasure appears only if
the player is in close proximity to it. Once it appears the
player can collect the item by moving the mobile device into
it. Collecting treasure increases the players score by +1. The
player is instructed to explore the level until they believe they
have discovered all the hidden treasure and then move to the
exit. Only when the player moves into the exit square, they are
shown the 2D puzzle (in the shuffled order) and all the treasure
pieces that were not collected appear to them as white squares.
This is when the player will get confirmation if all pieces were
collected or not. This design decision ensures that players are
motivated by the exploration and discovery of dynamic content
within the game space. Informing the player beforehand of the
maximum number of pieces hidden in that level would reduce
the sense of exploration and discovery, which is an important
aspect of these real-world games.
The player wins the round once the puzzle is completed.
The game has been designed in such a way that all game
levels can be completed. However, the reward for the player
varies depending on the amount of treasure they collect, which
corresponds to the amount of the picture they get to appreciate
at the end of the game round. The game interactions were
designed to be simple so as to keep the cognitive load from
the UI on the players low. The game was developed in Unity,
using their experimental AR interface to handle the device and
environment tracking for the game. The Unity asset store was
used for game assets used in the game. The mobile device
used for development and testing was the Google Pixel 2 XL.
IV. EXPERIMENTAL SETTINGS
The study design was informed by previous studies that
model PX for content creation [2], [3], [5]. With this protocol,
we build a data-set of player movement data and corresponding
emotional preferences in AR game sessions. 42 volunteers (17
female and 25 male) aged 18-44 (51% were 18-24, 21% were
25-29, 15% were 30-34, 10% were 35-39, 3% were 40-44)
took part in this study. When asked about prior experience
playing AR games 45% of subjects had no prior experience.
In the remaining 55% of subjects: 29% reported having only
one experience in the past, 24% played a few times before,
and 2% of participants played AR games regularly.
A. Experimental Protocol
The study consisted of a number of sessions of the same
format. In each session, participants played 2 rounds of the
game with different CGFs in each round (resulting in varying
levels that created a spectrum of emotional responses from
players). Participants were not given any constraints on how
to hold the phone (in portrait or landscape). They could
use either hand to hold the phone depending on comfort.
Most participants (except for 2) preferred portrait mode and
tended to prefer their dominant hand. As we are interested in
modelling a player’s emotional preference, pilot studies were
conducted to identify appropriate game features that could
create a diverse range of emotional responses from players.
The 2 chosen CGFs were:
• The Area of the Level (GA): 2 sizes of levels are
compared. Large Area (LA) levels are ≈ 30m×30m and
Small Area (SA) levels are ≈ 5m×5m. GA ∈ {LA, SA}
• Treasure in Level (GT ): 2 amounts of treasure are compared: Low Treasure (LT) with 9 and High Treasure (HT)
with 16 pieces respectively. GT ∈ {LT, HT}
Using 2 CGFs has resulted in 4(2 × 2) levels being compared: (1) LA×HT (2) SA×HT (3) LA×LT (4) SA×LT.
Since games are played in pairs, the total number of game pair
combinations is 6. In this study we have focused testing on
3(out of 6) of the game pairs:
(1) LA × HT vs (4) SA × LT
(2) SA × HT vs (3) LA × LT
(1) LA × HT vs (3) LA × LT
We do not explore the complete comparison space because
real-world optimization for player preferences would rely on
similar incomplete data-sets. The current choice of 2 binary
variables as CGFs is adequate for the purposes of our exploratory study. It would easily become unfeasible to collect
pairwise preferences of the complete comparison space if a
more complex set of CGFs is used.
At the end of each game pair, the participants were given
a 4-AFC protocol. This is a questionnaire that ranks the 2
games according to different dimensions. This study focused
on Boredom, Challenge, Excitement, Frustration and Fun [10]
as dimensions to measure player preference; since previous
research has shown that these states are relevant to digital
game-play. Following shows the 4-AFC protocol measuring
the dimension of Fun:
Please select 1 of the following options
1) Game 1 felt more Fun than Game 2
2) Game 2 felt more Fun than Game 1
3) Game 1 and Game 2 felt equally Fun
4) Neither of the two games felt Fun
The same format is used to measure each dimension of
preference. This data is used as ground truth for players’
preferences between pairs of games. The study began with a
briefing for each participant which included a training session
on the game and the structure of each session of the study. This
was followed by a trial session in the described format; data
from this session is discarded. For the trial session, 2 levels
were designed with different areas (LA, SA) containing 4 treasure pieces each. The trial allowed participants to familiarize
themselves with the game and study format. The trial was
followed by 3 experiment sessions; participants were given
a minute’s break between each. They were then debriefed
and the study was concluded. The order of sessions was
randomized and the order of game pairs was counterbalanced
to minimize ordering effects in the data collected.
B. Data Collection
During the study, player behaviour and preference data were
collected. As each session consisted of comparing 1 game pair,
each subject contributed 3 game pairs of preferences resulting
in 126 games pairs (252 individual games). However, due to
some technical crashes, only 117 game pairs were successfully
recorded and used in the data analysis. The following data was
collected from each pair:
a) Player Behaviour Data (P B): measured from player
movement in game sessions. The mobile IMU sensors record
position and rotation of the device during the game. This data
is recorded at a frequency of 64 Hz following guidelines from
Preece et al. [11] and the discrete-time signals are stored as
a 6-dimensional vector: α ∈ {PX, PY , PZ, RX, RY , RZ} for
position and rotation. This sampling frequency is high enough
to pick up both large (arm/hand movements, walking) and
small movements (hand tremors, body jerks). The player’s
score(S), which increases as the treasure pieces are collected,
is recorded at the same frequency.
b) Emotional Preferences Data (P F): The 4-AFC collects preference data between game pairs along dimensions of
Boredom, Challenge, Excitement, Frustration and Fun.
V. METHODS
The resulting data-set from the study was used to explore
preference learning approaches to modelling players’ preferences. The data is pre-processed and PBFs are extracted. These
features along with the CGFs were used to model players’
preferences. In order to better understand the effects of each
feature, and to explore to what extent noise from ordering
effects have biased the data, we first conduct statistical analysis
on the features.
A. Data pre-processing
Pre-processing reduces noise and redundancy in the data.
This stage is adapted from [12] and is broken into these
steps: Data Segmentation, Low Pass Filtering, Coordinate
Difference, and Dimensionality Reduction.
a) Data Segmentation: As we are only interested in
game behaviour, movement data from when players were
interacting with the mobile device before gameplay (e.g., while
confirming the placement of the AR level in the physical
space) was discarded.
b) Low Pass Filtering: The segmented data may be noisy
and contain unwanted high-frequency components. In order to
reduce this, we use a Gaussian filter with coefficients from
[12]: h =
1
16 [1, 4, 6, 4, 1]. The filter is a 1D convolution of the
Gaussian filter and each column of the raw data in α (details
in section IV) given by the following equation:
y(n) = X∞
t=−∞
x(t)h(n − t) = x(n) ∗ h(n) (1)
In eq 1, x is a column of the vector α (raw data), h is the
Gaussian filter and ∗ the convolution operation.
c) Coordinate Difference: Movement qualities such as
velocity(α˙), acceleration (α¨) and jerk4
(
...
α) are extracted for
each of the columns of the vector α [13].
α˙(t) = x(t) − x(t − 1) (2)
α¨(t) = ˙α(t) − α˙(t − 1) (3)
...
α(t) = ¨α(t) − α¨(t − 1) (4)
This step outputs 3 6-D vectors for velocity (α˙), acceleration
(α¨) and jerk (...
α). These 3 vectors contain data for velocity,
acceleration, jerk, angular velocity, angular acceleration and
angular jerk along the x, y and z axis. Analyzing the quality
of movement in this way minimizes the impact of interparticipant differences in holding the phone on the preference
predictions.
d) Dimensionality Reduction: In order to reduce the
dimensionality of the feature space, we use the Euclidean
norm of the x, y and z axis for velocity, acceleration, jerk,
angular velocity, angular acceleration and angular jerk. This
output 6-D vector along with the score is the final output
of the data pre-processing phase for each game, given by
β ∈ {V, A, J, RV, RA, RJ, S} at 64Hz.
B. Feature Extraction
PBFs are extracted from the pre-processed movement data:
V, A, J, RV, RA, RJ (first 6 dimensions of the time series
vector β), table I shows the 10 features that are extracted
for each dimension resulting in 60 movement features. The
S signal (last dimension of β) is used to compute 2 features:
• Completion (C): The fraction of the score at the end of
game divided by maximum possible score from game,
given by: C =
Score at end Game
Max Game score
• Score Rate (SR): The fraction of the score at the end
of the game and the time taken to reach it (note: this is
different from total time of the game), given by: SR =
Score at end Game
T ime to reach score
The final feature considered is the time of the game in
seconds (T), resulting in 63 PBFs. These, along with CGFs
(65 features in total) were used in the following analysis.
4
Jerk is the derivative of acceleration
TABLE I
EXTRACTED MOVEMENT FEATURES
Feature X Description
Xm Mean
Xstd Standard Deviation
Xsk Skew
Xkur Kurtosis
Xmin Minimum value
Xmax Maximum value
XD Max - Min
XtMin Time of Minimum value
XtMax Time of Maximum value
XtD Time of Max - Time of Min
C. Statistical Analysis
Statistical analysis was conducted to check for ordering
effects in the data and to understand the relationship between
features (PBFs, CGFs) and emotional preferences. The Chisquare test is used to check for ordering effects in preference
data, which is based on the number of times subjects expressed
a preference for the first or the second game in the pair. Chisquare test is also used to check for statistically significant
effects of the 2 CGFs on preferences as these are binary
categorical features. The Wilcoxon signed-rank test was used
to check for significant effects of the PBFs on preferences as
these are continuous features. All tests for significant effects
use a p-value < 1%.
We followed the method to compute correlation coefficients
from [5], given by c(z) = PNs
i=1{zi/Ns} where Ns is number
of pairs where subjects expressed clear preferences for one of
the two games (picking the first 2 options of the 4-AFC), and
zi = 1 when the subject preferred the game with the larger
value of the examined feature, and zi = −1 when the subject
chooses the other game. From the 117 game pairs that were
analyzed Ns is 59, 105, 89, 81, 106 for Boredom, Challenge,
Excitement, Frustration and Fun respectively. Variance in Ns
shows that subjects find it difficult to express a clear emotional
preference between game variants.
D. Preference Learning
Preference learning techniques are applied to explore to
what extent PBFs and CGFs can be used to predict players’
preferences. We use the large margin algorithm [14], which
was originally developed for a driving route recommendation
system. This technique has been previously applied in similar
studies of modelling PX [2], [3], [5]. We have also applied
feature selection techniques to improve model performance.
a) Large Margin Algorithm: This method aims to model
features of interest through a linear combination of a weighted
vector that binds preferences to features. This is given by
P(F) = FWT
, where P(F) is the subjects preference,
F is the extracted feature vector and W is the weights to
be optimized. As we are predicting pairwise preference, we
would like to predict P(FA) > P(FB) if a subject has a
preference for Game A over Game B. Here FA and FB are
PBFs and CGFs extracted from each of the games A and B
respectively. This inequality can be expressed through a linear
combination FAWT > FBWT
, which is further rewritten
as (FA − FB)WT > 0 or FDWT > 0, where FD is the
feature difference vector for the preference. The problem is
thus reformulated as a linear classification of estimating W,
where the input features are the feature difference between the
original feature space of the 2 game pairs being compared.
Previous research explores this as a binary classification
problem where FDWT ∈ [0, 1]: 0 is assigned to an instance
where the subject has a preference for Game A over Game
B and 1 for the opposite preference. This is accomplished
by either filtering the data to remove instances with no
preferences [14] or by forcing choice onto subjects via the 2-
AFC protocol [3]. We believe that for this exploratory study,
it would be beneficial to investigate both binary (via data
filtering) and ternary classifications where FDWT ∈ [0, 1, 2]:
2 is the class assigned to instances where the subject had
no preference (options 3 and 4 in the 4-AFC protocol). We
investigate this approach as it matches the format of the data
collected without filtering.
As we are interested in optimizing the weight vector W,
which can be used to linearly combine the feature space FD in
order to predict preferences, we check the performance of three
linear classifiers for this problem: logistic regression, linear
discriminant analysis (LDA) and support vector machines
(SVM). LDA has been used in a related study [3]. However,
it is unclear from previous work if other linear models can
outperform this approach. In this study, we evaluate model
performance using the sample accuracy and standard deviation
from 10-fold cross-validation (CV).
b) Feature Selection: Previous studies observe that
model performance improves through feature selection techniques [2], [3]. While there are a large number of approaches,
we use sequential forward selection (SFS) and sequential
floating forward selection (SFFS) in this study as they are
often used in similar work [2], [3]. In [2], SFS and SFFS
outperformed other techniques tested. SFS is a bottom-up
search algorithm that tries to find the best performing feature
set. It starts with the best performing single feature and
adds new features from the remaining set such that model
performance of the new set generates the best possible overall
performance over other potential features for addition. SFFS is
similar to SFS except that when a forward step is performed,
the algorithm also checks if a feature from the existing set can
be excluded in order to improve overall model performance.
VI. RESULTS
This section presents results from analyzing each dimension
of emotional preference with both statistical analysis and
preference learning techniques. This section concludes with
feature recommendations based on the results.
A. Statistical Analysis
This subsection describes results from order testing (to
check if the ordering of the game has created noise in
the preferences) and the correlation analysis of statistically
significant features.
TABLE II
STATISTICALLY SIGNIFICANT (P-VALUE < 1%) CORRELATION
COEFFICIENTS FOR BOREDOM.
Feature c(z)
Controllable Level Features
Area of level (GA) 0.339
Treasure in level (GT ) -0.322
Player Behaviour Features
Acceleration Mean (Am) 0.390
Acceleration Standard Deviation (Astd) 0.390
Maximum Acceleration (Amax) 0.458
Max-Min Acceleration (AD) 0.458
Jerk Mean (Jm) 0.424
Jerk Standard Deviation (Jstd) 0.458
Maximum Jerk (Jmax) 0.424
Max-Min Jerk (JD) 0.424
Velocity Mean (Vm) 0.390
Time (T) 0.390
TABLE III
STATISTICALLY SIGNIFICANT CGFS AND TOP TEN STATISTICALLY
SIGNIFICANT (P-VALUE < 1%) PBFS CORRELATION COEFFICIENTS FOR
CHALLENGE.
Feature c(z)
Controllable Level Features
Area of level (GA) 0.686
Player Behaviour Features
Time of Max Acceleration (AtMax) 0.467
Time of Max Velocity (VtMax) 0.476
Time of Max Ang. Acceleration (RAtMax) 0.504
Time of Max Ang. Jerk (RJtMax) 0.523
Maximum Ang. Velocity (RVmax) 0.467
Max-Min Ang. Velocity (RVD) 0.467
Time of Min Ang. Velocity (RVtMin) 0.467
Time of Max Ang. Velocity (RVtMax) 0.504
Score Rate (SR) -0.714
Time (T) 0.771
a) Boredom: Participants had a preference of Boredom
50.43% of the time. Order testing showed a significant (p =
0.001) effect: subjects tended to find the second game more
boring. Table II shows the significant PBFs and CGFs.
b) Challenge: Participants had a preference 89.74% of
the time. Order testing was not significant. Statistical testing
of the features showed that area of the level was the only CGF
that had a significant effect (details provided in table III) while
39 PBFs showed a significant effect. We report only the top
ten correlation coefficients in table III.
c) Excitement: Participants had a preference 76.06% of
the time. Order testing was not significant. Among all the
features, only Treasure in Level GT (a CGF) had a significant
effect with c(z) = 0.339.
d) Frustration: Participants had a preference 69.23%
of the time. Order testing was not significant. Feature tests
showed that area of the level was the only CGF that had a
significant effect (details in table IV) while 38 PBFs showed
a significant effect. We report only the top ten correlation
coefficients for Frustration in table IV.
e) Fun: Participants had a preference 90.60% of the
time. Order testing showed a significant (p = 0.002) effect,
TABLE IV
STATISTICALLY SIGNIFICANT CGFS AND TOP TEN STATISTICALLY
SIGNIFICANT (P-VALUE < 1%) PBFS CORRELATION COEFFICIENTS FOR
FRUSTRATION.
Feature c(z)
Controllable Level Features
Area of level (GA) 0.691
Player Behaviour Features
Maximum Acceleration (Amax) 0.481
Max-Min Acceleration (AD) 0.481
Time of Max Jerk (JtMax) 0.530
Time of Max Velocity (VtMax) 0.444
Time of Max Ang. Acceleration (RAtMax) 0.481
Time of Max Ang. Jerk (RJtMax) 0.555
Minimum Ang. Velocity (RVmin) -0.481
Time of Max Ang. Velocity (RVtMax) 0.456
Score Rate (SR) -0.679
Time (T) 0.802
subjects tended to find the first game more fun. Among
the CGFs, Area of level (GA) had a significant effect with
c(z) = −0.198. Among the PBFs Completion (C) had a
significant effect with c(z) = 0.245.
B. Machine Learning
In this section, we present results from preference learning
techniques. The extracted features are used to predict preferences. A summary of findings is shown in Table V which provides the accuracy and standard deviation from 10-fold CV of
the Logistic Regression, LDA and SVM classifiers, to predict
the various emotional dimensions of preferences in both binary
and ternary classification scenarios. Corresponding accuracies
of the best performing feature subset from the feature selection
techniques (SFS, SFFS) along with the number of features in
the subset have also been provided in Table V. It has been a
common observation across all dimensions and types of linear
classifiers that the base (all 65 features) performance without
feature selection performs poorly with very high standard
deviation (as high as ±28.55% for the ternary Fun LDA
classifier). However, all base classifiers perform higher than
random chance (50% for binary and 33.34% for ternary).
a) Boredom: The best binary classifier was SVM with a
subset of 14 features found with SFFS - the performance was
86.33 ± 10.2%. The best ternary classifier was SVM with a
subset of 21 features found with SFS - the performance was
60.29 ± 13.8%.
b) Challenge: The best binary classifier was SVM with
a subset of 5 features. Both SFS and SFFS found the same
feature set - the performance was 93.36 ± 4.4%. The best
ternary classifier was SVM as well with the same subset of 5
features (found with both SFS and SFFS) - the performance
was 84.85 ± 5.5%.
c) Excitement: The best binary classifier was LDA with
a subset of 15 features found with SFFS - the performance
was 75.61±12.5%. The best ternary classifier was SVM with
a subset of 14 features found with SFFS - the performance
was 56.92 ± 10.7%.
d) Frustration: The best binary classifier was SVM with
a subset of 24 features found with SFFS - the performance was
93.89±6.1%. The best ternary classifier was SVM as well with
a subset of 11 features found with SFFS - the performance was
75.43 ± 8.3%.
e) Fun: The best binary classifier was SVM with a subset
of 16 features found with SFFS - the performance was 79.21±
9.7%. The best ternary classifier was SVM with a subset of 10
features found with SFFS - the performance was 69.42±7.0%.
C. Feature Recommendations
In this subsection, we present a list of feature combinations
that can be used as a starting point in similar work to predict
player preferences. The recommendations are based on a
grounded analysis of features in terms of statistical effects and
likelihood of each being selected in the best performing feature
subset. We have observed 2 common features in predicting
all the emotion dimensions, given by: Θ ∈ {Amin, RVmin}.
A number of feature sets were also found that could predict
4(/5) dimensions. A set of 4 features can be used to predict
Boredom, Challenge, Excitement and Frustration (not Fun),
given by: Λ ∈ {Am, Astd, Amax, Vm}. A single feature can
be used to predict Boredom, Challenge, Frustration, Fun (not
Excitement), given by: Π ∈ {GA}. Similarly, feature sets
emerge that can predict 3(/5) dimensions. A single feature was
found to be able to predict Challenge, Frustration, Fun, given
by Φ ∈ {C}. A single feature can be used to predict Boredom,
Challenge, Frustration, give by: Ψ ∈ {Jm}. Common feature
sets also emerge that can predict pairs of emotions. A large
set of features could predict Challenge and Frustration, given
by: Ω ∈ {T, AD, Jstd, RAtM in, RJtM in, RVmax}. Another
pair of features could predict Boredom, Excitement, given
by: ∆ ∈ {GT , RJtD}. Feature recommendations for each
dimension of preference are given by the composition of
the sets of features presented above, illustrated in fig 2.
For instance, Challenge is a set of 15 features, given by:
CH ∈ {Θ,Λ, Π, Ψ, Φ, Ω}. Showing the recommendations as
compositions of other feature sets allows us to appreciate
important relationships across emotions. For instance, a total
overlap in the features that predict Challenge and Frustration
is observed (implications are discussed in the next section).
Fig. 2. The figure shows how recommended feature sets for the various
dimensions of emotional preference can be expressed as compositions of
important feature sets.
TABLE V
SUMMARY OF RESULTS FROM THE PREFERENCE LEARNING TECHNIQUES INCLUDING FEATURE SELECTION FOR BOTH BINARY AND TERNARY
SCENARIOS. FOR EACH CLASSIFIER, THE NUMBER OF FEATURES USED, THE SAMPLE ACCURACY AND STANDARD DEVIATION FROM 10-FOLD CV ARE
SHOWN. THE BEST PERFORMING BINARY AND TERNARY CLASSIFIER FOR EACH EMOTION HAS BEEN HIGHLIGHTED.
Log. Reg. LDA SVM
F# Acc ±SD F# Acc ±SD F# Acc ±SD
Boredom Binary All 65 64.33% 15.8% 65 50.67% 28.6% 65 78.00% 12.9%
Ns = 59 SFS 28 79.67% 9.9% 27 80.00% 12.5% 15 84.67% 9.1%
SFFS 29 79.33% 15.3% 40 84.67% 11.8% 14 86.33% 10.2%
Ternary All 65 45.71% 17.0% 65 37.58% 12.9% 65 47.64% 8.0%
Ns = 117 SFS 3 57.37% 14.7% 6 58.21% 13.0% 21 60.29% 13.8%
SFFS 36 57.45% 16.3% 6 58.21% 13.0% 32 58.69% 10.9%
Challenge Binary All 65 81.00% 10.3% 65 74.54% 13.1% 65 81.10% 10.0%
Ns = 105 SFS 15 91.54% 5.0% 4 91.45% 5.0% 5 93.36% 4.4%
SFFS 3 91.45% 5.0% 4 91.45% 5.0% 5 93.36% 4.4%
Ternary All 65 67.70% 12.7% 65 62.80% 14.1% 65 72.86% 9.1%
Ns = 117 SFS 2 82.20% 5.2% 12 82.26% 6.0% 5 84.85% 5.5%
SFFS 2 82.20% 5.2% 12 82.26% 6.0% 5 84.85% 5.5%
Excitement Binary All 65 55.44% 15.8% 65 57.81% 20.5% 65 56.39% 8.2%
Ns = 89 SFS 12 73.64% 13.6% 9 72.39% 13.7% 8 68.53% 9.6%
SFFS 9 73.64% 14.5% 15 75.61% 12.5% 12 71.89% 10.5%
Ternary All 65 44.05% 12.9% 65 36.41% 11.7% 65 43.01% 6.9%
Ns = 117 SFS 31 54.99% 14.1% 20 54.88% 11.4% 2 55.17% 14.7%
SFFS 15 53.05% 13.3% 11 55.78% 11.2% 14 56.92% 10.7%
Frustration Binary All 65 85.38% 8.8% 65 68.29% 14.1% 65 82.60% 8.4%
Ns = 81 SFS 1 90.41% 8.9% 1 90.41% 8.9% 4 92.78% 5.9%
SFFS 1 90.41% 8.9% 34 92.92% 9.5% 24 93.89% 6.1%
Ternary All 65 50.57% 14.4% 65 45.57% 20.4% 65 59.64% 14.6%
Ns = 117 SFS 11 71.83% 10.6% 16 72.65% 11.6% 16 72.92% 8.3%
SFFS 13 73.57% 9.8% 20 74.05% 10.0% 11 75.43% 8.3%
Fun Binary All 65 65.62% 13.9% 65 58.47% 11.9% 65 60.37% 5.2%
Ns = 106 SFS 42 73.68% 8.9% 35 74.33% 6.5% 16 74.68% 6.8%
SFFS 32 76.26% 8.6% 34 77.59% 11.4% 16 79.21% 9.7%
Ternary All 65 59.56% 9.2% 65 47.53% 11.7% 65 54.77% 5.0%
Ns = 117 SFS 4 65.75% 7.8% 1 65.67% 8.1% 9 67.68% 6.8%
SFFS 32 65.94% 8.4% 18 68.99% 9.0% 10 69.42% 7.0%
VII. DISCUSSION
The results and recommendations made indicate that combinations of CGFs and PBFs can be used to accurately predict
dimensions of emotional preferences. We have chosen to compare several linear classifiers and have found SVM classifiers
(unexplored in previous research for this problem) were the
best performing classifiers for both accuracy and stability,
indicated by higher accuracy and lower standard deviation of
10-fold CV accuracies. Although all classifiers perform better
than random chance, we find that binary classifiers outperform
ternary classifiers in both accuracy and stability.
Boredom, Excitement and Fun are difficult to model, which
is observed in statistical analysis as well as the low accuracy
and stability. Predicting Challenge and Frustration shows
higher accuracy and stability. Due to the small data-set, we
believe that classifiers for Boredom, Excitement and Fun show
over-fitting due to a high standard deviation of 10-fold CV
accuracies. The performance of Challenge and Frustration
shows more acceptable stability with both binary and ternary
classifiers showing a variance of ≈ 5%. We believe that
this variance will be further reduced if a larger data-set is
used. Our results are similar to results from other studies
that model players’ preferences [2]. Although the authors
investigate Super Mario in their work, they find that Fun and
Boredom were the most difficult to predict, while Challenge
and Frustration were the best performing classifiers.
We propose that our observations are due to the underlying
relationship between game activities and the specific emotional
dimension. Some theories [15] consider emotions as being
constructed from more fundamental properties called Valence
and Arousal: Valence is the amount of goodness or badness
in experiences, while Arousal is the psychological state of
being awake. Fun, Excitement, and Boredom are more resonant with the emotional dimensions of valence. Frustration
and Challenge are resonant with the emotional dimension of
arousal. Frustration is a construct of negative valence and
high arousal, while Challenge is strongly linked to player
performance and high arousal states. As our approach uses
movement data, this information medium could be more useful
to detect variable arousal rather than variable valence based
emotional states. Although it is useful to use valence and
arousal to interpret these results, the current approach of
asking players about preferences across easily understandable
emotions has obvious advantages as it is more intuitive for
people to compare 2 experiences based on Fun or Frustration
rather than valence and arousal. Studies about emotions [15]
also tell us that people are different in their ability ‘to represent
their experiences as categorically distinct events’ and this
ability is influenced by context and language abilities. This
is observed in our study by the variable proportions of clear
preference across the emotions tested. For this reason, we also
caution that the nature of the game task can bias players’
interpretation of the preferential comparison being made.
Our game requires considerably high amounts of walking;
we believe that our results are applicable to similar AR games
that require movement in local space. The techniques proposed
in this paper have considerable potential to create content that
is optimized for an ideal balance of Challenge and Frustration
(i.e. the best balance of most challenging and least frustrating).
Currently, we explore a simple game design space and it is
possible that Frustration and Challenge are being predicted by
the same underlying feature correlations. In this game, most
participants appear to prefer a large amount of Treasure (more
fun, exciting and less boring) and do not prefer walking a
large amount (more challenging and frustrating). In this case,
it would be impossible to find a game experience that is both
challenging but not frustrating. It would be interesting to see
how this approach scales in more complex game design spaces.
This can easily be achieved by using a CGF set of higher
complexity, for example, GA ∈ {XS, S, M, L, XL}. This set
contains possible values for the area of the game parameter
that allows for a more diverse range of levels moving from
small to large.
This study serves as a starting point in a better understanding of how player behaviour in AR environments can be used
to model their preferences. An aspect of this problem that
is unresolved is guidelines of following a binary or ternary
approach to classification. Binary classification performs better and is more stable. However, this is unsurprising since
it models a simpler problem. This advantage over ternary
classification seems preferable and could be accomplished by
forcing a binary choice onto participants referred to as the
2-AFC [3]. A critique of this approach is that it seems a
naive way of achieving better performance and could prove
detrimental to optimization for true player experience.
Our work is built on studies of detecting emotions from
movement and we apply it to predict emotional preferences, a
complex problem that we have begun probing. Future research
in addressing the discussed gap in establishing ground truth,
improved feature extraction, and different models to address
this problem (clustering players, non-linear classifiers or models for time-series data) would increase our understanding.
Current trends of casual play on mobile devices show the need
to explore other sensor information available to model players’
emotional preferences. For instance, location and time of day
are important features to consider for personalization in mobile
gaming.
VIII. CONCLUSION
To the best of our knowledge, this work is the first known
study that exploits mobile sensor information to explore to
what extent this can be used to personalize game experiences.
Investigation in this area will allow AR games to become more
engaging. Niantic (makers of PoGO) has announced the launch"
Do this paper explain why AI and machine learning are critical to AR gaming?