Hello in this work we propose a novel approach
for screening alcohol dependence using user game interaction patterns
Long term excessive alcohol use can cause significant brain damage
resulting in cognitive deficits and reduced motor control
According to 2018 Public Health England, more than half a million people were
alcohol-dependent drinkers However only 18 per cent of them ever
received treatment
Given that current diagnostic and
screening measures for alcohol dependence
rely on self-reporting and the procedure are time-consuming and must be
administered by trained clinicians these limitations led a call for an
alternative approach
Alcohol dependence is often manifested
with specific cognitive decline and reduced fine motor control
We consider that both of these changes can influence the way users interact
with mobile games
In this work, we use three casual mobile
games running on mobile devices, i.e, Tetris
Fruit Ninja and Unblock Puzzle
Three different
feature sets were extracted during user-game interaction
These include gameplay performance touch interaction and device motion
The study sample was drawn from 40 male patients
diagnosed with alcohol dependence
A control group
of 40 age and gender-matched healthy adults
were recruited through public advertising
In the experiment, participants' cognitive
function was examined using standardised cognitive tests
Next, participants were given an introduction on how to play all the three games
to reduce the effect of participants' prior game experience,
they were asked to familiarise themselves with the games
each game for 5 minutes
Afterwards, participants were asked to play each
game for 10 minutes in random orders to avoid order effects
User-game interaction patterns were then collected during the gameplay
Based on the data collection, three sets of features were computed
using the entire data samples
For the touch interaction
data cleansing was also carried out to remove the faulty touch data points that
might occur from unintentional touches while participants
held the phone in their palm
Three supervised machine learning
methods were employed to develop binary classifiers to identify patients
from healthy control
As our observed class label were
balanced, accuracy was used to evaluate the model performance
Stratified 10-fold cross-validation was used to generate training and test sets
with a balanced distribution of classes
Comparisons were made on the model performance over five different feature
sets to investigate the effect on accuracy
the three plots on the slide demonstrate the model
performance using the data of each game as input
The x-axis represents the feature set while the y-axis represents the mean accuracy
The bar colour represents the
classification algorithms
By comparing the overall results using
combinations of multiple feature sets as input
improve the model performance in classification
accuracy as compared with using only a single set of features
It is noted that logistic regression shows the highest mean accuracy of 0.96
when using the ensemble of touch and sensor features from Tetris
Given that an ideal alcohol screening measure should provide
accurate results within a short time, we further examined the model performance on
3-minute samples
To this end, the original 10-minute data
was truncated to the first 3-minute samples
Logistic Regression and Random Forests were trained on the combination of all feature sets
For both Tetris and Unblock Puzzle, the Random Forest
classifiers on 3-minutes data shows even better results when compared
with the model on 10-minute samples
In particular, the model using 3-minute samples of Unblock Puzzle could achieve 95% accuracy
In contrast, the performance of Logistic Regression seems to drop
significantly when using 3-minute samples
We then further examined the sensitivity and specificity of our classifier,
as they are more common measures for assessing the performance of clinical tests
The Random Forest classifier on 3-minute samples of Unblocked Puzzle
still demonstrates promising results
In particular, the model achieved 95% sensitivity and specificity
Our key finding
With regard to classification performance, our game-based approach outperformed
both conventional paper-based screening tools and the EEG-based methods
In addition
the game-based screening instrument is robust, fast, portable and requires
only minimal effort to implement
In contrast, the EEG data acquisition
often takes a long time for equipment setup and calibration
which can be administered only by trained clinical staff within clinical premises
One possible implication is that our game-based measure could help facilitate
the brief self-screening in a waiting area prior to seeing the doctor
The result from the measure could assist the
therapist in decision making and planning for further examination
In addition, patients may be prescribed the mobile
game-based screening application to monitor for early signs of relapse to
harmful alcohol use Thank you