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Citation: Vajs, I.; Kovi´c, V.; Papi´c, T.;
Savi´c, A.M.; Jankovi ´c, M.M.
Spatiotemporal Eye-Tracking Feature
Set for Improved Recognition of
Dyslexic Reading Patterns in
Children. Sensors 2022,22, 4900.
https://doi.org/10.3390/s22134900
Academic Editors: Jordi Solé-Casals,
César F. Caiafa, Zhe Sun, Pere
Marti-Puig and Toshihisa Tanaka
Received: 3 June 2022
Accepted: 27 June 2022
Published: 29 June 2022
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4.0/).
sensors
Article
Spatiotemporal Eye-Tracking Feature Set for Improved
Recognition of Dyslexic Reading Patterns in Children
Ivan Vajs 1,2, *, Vanja Kovi´c 3, Tamara Papi´c 4, Andrej M. Savi´c 1and Milica M. Jankovi´c 1
1
School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia;
andrej_savic@etf.rs (A.M.S.); piperski@etf.rs (M.M.J.)
2Innovation Center, School of Electrical Engineering in Belgrade, Bulevar Kralja Aleksandra 73,
11120 Belgrade, Serbia
3Faculty of Philosophy, University of Belgrade, ˇ
Cika-Ljubina 18-20, 11000 Belgrade, Serbia;
vanja.kovic@f.bg.ac.rs
4Faculty of Technical Sciences, University Singidunum, Danijelova 32, 11000 Belgrade, Serbia;
tpapic@singidunum.ac.rs
*Correspondence: ivan.vajs@ic.etf.bg.ac.rs; Tel.: +381-11-3218-455
Abstract:
Considering the detrimental effects of dyslexia on academic performance and its common
occurrence, developing tools for dyslexia detection, monitoring, and treatment poses a task of
significant priority. The research performed in this paper was focused on detecting and analyzing
dyslexic tendencies in Serbian children based on eye-tracking measures. The group of 30 children
(ages 7–13, 15 dyslexic and 15 non-dyslexic) read 13 different text segments on 13 different color
configurations. For each text segment, the corresponding eye-tracking trail was recorded and then
processed offline and represented by nine conventional features and five newly proposed features.
The features were used for dyslexia recognition using several machine learning algorithms: logistic
regression, support vector machine, k-nearest neighbor, and random forest. The highest accuracy
of 94% was achieved using all the implemented features and leave-one-out subject cross-validation.
Afterwards, the most important features for dyslexia detection (representing the complexity of fixation
gaze) were used in a statistical analysis of the individual color effects on dyslexic tendencies within
the dyslexic group. The statistical analysis has shown that the influence of color has high inter-subject
variability. This paper is the first to introduce features that provide clear separability between a
dyslexic and control group in the Serbian language (a language with a shallow orthographic system).
Furthermore, the proposed features could be used for diagnosing and tracking dyslexia as biomarkers
for objective quantification.
Keywords:
developmental dyslexia; reading; screening; colored background; eye-tracking; feature
extraction; machine learning; support vector machine; k-nearest neighbors; random forest; logistic
regression
1. Introduction
Individual differences in learning to read originate from biological and environmental
factors, which shape the development of the brain systems involved in the reading pro-
cess [
1
]. Dyslexia, a specific learning disorder with impairments in reading [
2
] refers to
a pattern of learning difficulties characterized by problems with accurate or fluent word
recognition, poor decoding, and poor spelling abilities. Up to 20% of the general population
may exhibit some degree of these difficulties [
3
], while about 7% of people are affected
heavily enough to qualify for a dyslexia diagnosis [
4
]. Due to its nature, dyslexia is typically
diagnosed only after children have started to learn to read, when it becomes evident that
they are struggling to keep up with their peers [
5
]. At this point, the pupils with dyslexia
are already at risk of falling behind because reading is essential for school achievement
in most subjects. Moreover, children with poor reading skills are also at an increased
Sensors 2022,22, 4900. https://doi.org/10.3390/s22134900 https://www.mdpi.com/journal/sensors
Sensors 2022,22, 4900 2 of 18
risk of social, emotional, and mental health problems, such as school dropout, attempted
suicide, incarceration, anxiety, depression, and low self-concept [
6
]. Therefore, it would be
invaluable if children with dyslexia or at risk for dyslexia could be identified and involved
in prevention and treatment programs as early as possible.
The diagnosis of dyslexia, especially at its early stages, has proven to be a complex
task, especially because of the lack of a strict procedure for dyslexia screening [
7
]. Being
able to diagnose dyslexia and create a tool that can objectively quantify certain dyslexic
tendencies has proven to be quite important so that the diagnosis process could be as
objective and reliable as possible [8].
Recently, Carioti et al. have shown that in developmental dyslexia research (pub-
lished from 2013 to 2018), 67.4% of studies were performed on languages that could be
considered to have a deep orthographic system [
9
]. Considering this, performing dyslexia
research on languages that have a shallow orthographic system could be considered quite
important, not only because of their underrepresentation but because reading in a shallow
orthographic system is easier, making dyslexia even harder to diagnose. Dyslexia diagnosis
is a challenging task in the Serbian language (with one-to-one grapheme-phoneme pairs),
which belongs to the group of languages with a shallow orthography.
In this paper, the study performed on native dyslexic and non-dyslexic Serbian speak-
ers is presented. Novel spatiotemporal eye-tracking features were introduced, and the
classification results using various machine learning (ML) algorithms were compared with
the results obtained using conventional eye-tracking features. The difference between the
subject classes (dyslexic and non-dyslexic) was analyzed using statistical tests for different
color configurations in order to examine the influence of the color configuration of the read-
ing material on subject class separability. Statistical analysis was also performed within the
dyslexic subject group in order to analyze the influence of color configuration on reading
performance and to determine whether a given color could influence the eye movement
features in a manner indicating facilitation or aggravation of the reading task in subjects
with dyslexia.
The contributions of the performed research are as follows:
•
Development of a novel feature set for describing and quantifying dyslexic tendencies
in the Serbian language;
•
Statistical and classification analysis, showing the potential of the proposed features
to be used as indicators of dyslexic tendencies;
•
An analysis of the influence of colored backgrounds and overlays on reading patterns
using a selection of the proposed features that have shown to be the most indicative of
dyslexic reading patterns.
2. Related Work
Dyslexia is diagnosed by tests that include reading and writing assessments, among
other evaluations, and are standardized by experts on a large number of subjects [
10
,
11
].
The advancement of technology has made the digitalization of these tests possible, and it
has also contributed to the objectivity of the testing as certain quantifiable metrics can be
obtained from digitalized dyslexia tests [12–14].
Different screening methodologies can be performed to distinguish dyslexic from
non-dyslexic subjects. Brain-imaging methodology most prominently focuses on functional
magnetic resonance imaging during reading [
15
,
16
] and diffusion tensor imaging [
16
,
17
],
which both show, respectively, the functional or morphological differences between the
dyslexic and the control group. Brain activity can be monitored using electroencephalogra-
phy (EEG) as well, either on its own [
18
–
20
] or in combination with other biometric signals,
such as heart rate, electrodermal activity (EDA), and eye tracking [21–24].
The analysis of reading and eye movement patterns is often performed in dyslexia
research. Temelturk et al. in [
25
] performed a systematic review of 25 papers that in-
clude binocular eye-tracking during linguistic and non-linguistic tasks in children from
5–17 years
of age with dyslexia and with typical development. The review aimed to com-
Sensors 2022,22, 4900 3 of 18
bine the knowledge from the existing literature that observed the binocular coordination in
children with dyslexia by describing the normative development of stable binocular control.
The findings of the review indicate clearly that there is poor binocular coordination in
children with dyslexia but that the results associated with different task characteristics were
not as consistent. Another study focused on detecting dyslexia based on reading patterns
was presented by Wang et al. in [
26
]. A neural network was developed that was used to
predict whether or not the subject had developmental dyslexia, based on the data gathered
from 399 Chinese children. The dataset included children aged 7–13, 187 with dyslexia and
212 controls. The authors report an achieved accuracy of 94%, claiming that the reading
accuracy was the feature that had the strongest factor in detecting dyslexia, but the phono-
logical awareness, the accuracy rate of pseudo characters, the morphological awareness,
the reading fluency, the rapid digit naming, and the reaction times of noncharacters made
important contributions to the classification as well.
Eye tracking is often used in the practical diagnosis of dyslexia as it provides a direct
insight into the visual sampling strategy. The eye movements of subjects with dyslexia
show an erratic gaze pattern that can be quantitatively described by features and used for
further development of the algorithms for automatic dyslexia recognition [27].
Rello et al. in [
28
] claim to be the first to attempt classifying dyslexia based on eye-
tracking features using machine learning. The language of the text used in the experiment
was Spanish, and 97 subjects were included (48 with dyslexia), with the subject age ranging
from 11 to 54. Each subject read 12 different texts, each presented in a different font type, on
white paper with black letters. A support vector machine (SVM) classifier was implemented,
and the features used as inputs were the age of the participant, mean duration and the
total number of fixations, total reading time, etc. The model was evaluated using 10-fold
cross-validation, and an accuracy of 80.18% was achieved.
A study with a larger number of participants and a more in-depth feature analysis
was performed in [
29
]. The data were gathered from 185 subjects (97 with dyslexia),
with ages ranging from 9 to 10, who read a single text written in the Swedish language.
The text was presented on white paper with black letters, and a total of 168 eye-tracking
features were considered. The features were derived from both version and vergence [
30
],
the regressive and progressive movements, the saccades, the fixations, the duration of
the event, the distance spanning the event, the accumulated distance of an event, the
accumulated distance over all subsequent positions, etc. Considering the large number
of features, a recursive feature elimination (RFE) algorithm was implemented to reduce
the number of features. An SVM classifier was used, and it was evaluated using 10-fold
cross-validation, which was repeated 100 times to ensure the stability of results in terms of
dataset splitting. The highest achieved accuracy was 95.6%, and it indicates that a large
number of subjects in combination with a wide range of observed features enables a reliable
classification. This paper also effectively performed subject-wise evaluation, where the
data from a given subject are either in the training or test, creating an evaluation scenario
similar to a real use case [31].
Prabha et al. [
32
] analyzed the dataset introduced in [
29
] using several ML algorithms.
Only the features extracted from fixations, in combination with an RFE feature selection
algorithm, were used for the classification. The authors implemented an SVM classifier
(with four different kernel configurations), a k-nearest neighbors (KNN), and a random
forest (RF) algorithm and achieved the highest accuracy of 95% by KNN. In their further
work [
33
], Prabha et al. focused on analyzing the same dataset, but with new ML algorithms,
such as particle swarm optimization (PSO)-based SVM hybrid kernel (hybrid SVM–PSO),
SVM, RF, logistic regression (LR), and KNN. They also observed features extracted from
both saccades and fixations and obtained an accuracy of 95.6% with the hybrid SVM–PSO
model. Prabha et al. also focused on observing eye-tracking feature sets and several other
ML algorithms in their work performed on the same dataset [
34
,
35
], obtaining similar
results, although a slightly higher accuracy of 96% in [
35
] using a hybrid SVM–PSO model.
Sensors 2022,22, 4900 4 of 18
A study including 69 children (32 with dyslexia) was conducted in [
36
]. The children
were aged 8.5–12.5 and read two text paragraphs in Greek. The authors implemented
several ML algorithms (KNN, SVM, and naïve Bayes) and observed a wide range of
eye-tracking features. The best-obtained accuracy of 97% was achieved using only three
features, saccade length, the number of short forward movements, and the number of
repeatedly fixated words.
A holistic approach for dyslexia detection based on a convolutional neural network
(CNN) was implemented in [
37
]. The authors used the dataset from [
29
], but rather than
extracting features, they used gaze coordinate data as a direct input to the CNN and
implemented several padding algorithms to make the data sequences the same length. The
achieved accuracy results of 96.6% (obtained with a modified cross-validation evaluation)
show that, given the right data encoding, deep learning algorithms can provide very
reliable dyslexia detection based on eye movement data.
Weiss et al. [
38
] analyzed the lateralization of early orthographic processing during
natural reading in subjects with dyslexia. The authors recorded the eye-tracking and
EEG activity of the subjects, 24 subjects with dyslexia (mean age 24.8) and 24 control
subjects (mean age 23), during the reading of isolated sentences in their native (Hungarian)
language, with various spacing between letters. The statistical analysis of the EEG and the
eye-tracking parameters performed in the paper has shown several interesting findings.
Increased spacing between letters was shown to reduce the silent reading speed in both
subject groups, in contrast to the beneficial effects on oral reading found in previous
work. Furthermore, the authors found that the early left-hemispheric lateralization of
orthographic processing during natural reading depends on the rank of fixations and that
it is most prominent when reading on the default letter spacing in control readers, as well
as that it deteriorates in subjects with dyslexia.
The detection of developmental dyslexia using machine learning and eye movement
data was performed in [
39
]. The authors observed a group of 165 subjects with an average
age of 12.5. Of the chosen subjects, 30 met the criteria for a reading disorder based on
choosing the 10th worst percentile of the reading fluency performance score, which was
used to label them as dyslexic. The language used in the reading experiment was Finnish
(the subjects’ native language), and a variety of eye-tracking features were observed. An
RF algorithm was used for feature ranking, and an SVM was used for subject classification
based on the selected features. The overall accuracy of 89.7% was achieved using five-fold
cross-validation.
El Hmimdi et al. [
40
] performed research on predicting a dyslexia diagnosis as well
as reading speed from eye movement data in both reading and non-reading tasks. The
authors used eye movement measures from four different setups, gathered from 46 dyslexic
subjects (average age 15.5) and 41 control subjects (average age 14.8), recruited from schools
in Paris. A vergence, saccade, and two reading tests were performed by each subject,
and several eye-tracking measures were derived from the obtained data. Based on the
obtained features, a variety of ML algorithms were implemented, and the findings showed
an accuracy of 81.25% percent when using the data from the reading tests and 81.25%
and 77.3% accuracy from the two no-reading tests, respectively. The prediction of reading
speed was also performed on each of the feature sets from the two reading tests and two
no-reading tests, showing that the reading speed can be predicted more accurately from
one non-reading task than from the two reading tasks.
Vajs et al. [
41
] presented a CNN solution for dyslexia detection based on the VGG16
neural network architecture. The eye-tracking data were gathered from 30 subjects (ages
ranging from 7–13), 15 with dyslexia, and 15 controls. The subjects read the text in their
native language (Serbian) on different colored backgrounds and overlays, and the raw eye-
tracking data were segmented, visualized, and used in the form of colored images as inputs
to the CNN model. The model was evaluated using leave-one-out subject cross-validation,
and an accuracy of 87% was achieved.
Sensors 2022,22, 4900 5 of 18
3. Materials and Methods
3.1. Dataset and Experiment Description
The data analysis in this paper was conducted on the dataset described in our previous
research [
22
,
41
]. The data were gathered from 30 subjects, 15 diagnosed with dyslexia and
15 control subjects (age: 7–13, gender: 19 female, 11 male), during a study approved by
the ethical committee of the Psychology Department of the University of Niš (a branch of
the Serbian Psychology Association), experimental procedure No. 9/2019. The subjects
could withdraw from the test at any time. In consultation with a certified speech therapist,
the subjects with dyslexia were selected from several elementary schools in Belgrade. The
control subjects were selected randomly from three elementary schools in Belgrade. The age
range in the group with dyslexia was 7–13 years, of which 4 were male and 11 were female,
with an average age 9.93. The age range in the group without dyslexia was
7–13 years
, of
which 7 were male and 8 were female, with an average age 9.67. All of the children in the
sample, both dyslexic and non-dyslexic, had normal (or corrected to normal) vision. The
children did the study in the morning hours during the regular school schedules.
During the experiment, the children were alone in an isolated, quiet, and bright room
with the experimenter, sitting on a chair at a table in front of a computer monitor and
keyboard. The screen size was 48 cm
×
27 cm, the brightness was set to 90%, the distance
from the screen was 62 cm; this was the same for all the participants. Additionally, we
used the chin-rest so that the position of the head/eyes relative to the monitor was the
same. During the experiment, each subject read 13 segments of the text extracted from
a standardized story for elementary school called “Saint Sava and the villager without
happiness”. At the beginning of the experiment, the subjects were instructed to read the
text quietly for themselves from the stimuli presentation shown and to press the space
button for the next slide of the stimuli presentation. The experiment was run applying the
pseudo randomization of color background/overlay order, always starting with a referent
slide (black text on white background). No other color was fixed/related to a certain text.
Therefore, in this way, any other factors apart from the actual color would be averaged out
(paragraph complexity such as vocabulary, syntax, etc., as well as semantic/affective con-
tent). The text was prepared and presented within the SMI Experiment Center software 3.7,
keeping the same size/font for each slide, centrally presented with approximately the same
length. All the colors (color shades) used for designing the slides (stimuli) were defined
within the RGB color model, and each individual color was expressed as an RGB triplet
([R,G,B]), where the value of each additive primary color component can vary from 0 to
255. A list of background shades in the slides with colored backgrounds (and black text)
with the associated numerical values of their RGB triplet is stated in Section 2.3 Experiment
Design of our previous study [
21
]. An example of the test boards used in the experiment is
attached in the Supplementary Materials. The reading of each text segment, for one subject,
will be called “a trial” in the further text, although 30 subjects were included in this study,
each with 13 trials. The trials with insufficient focus on the displayed text (reading time
less than 5 s) were excluded, resulting in a total of 378 trials used for further analysis.
Several biometric parameters of the subjects were monitored during the reading task
using a multimodal sensor hub [
21
]. The hub performed heart rate monitoring, EEG,
EDA monitoring, and eye tracking. This study, however, was focused on the eye-tracking
aspect of dyslexia, recorded by an SMI RED-m 120 Hz portable remote eye tracker (iMo-
tions, Copenhagen, Denmark). Eye-tracker calibration was conducted in the SMI BeGaze
software 3.7 (SensoMotoric Instruments, Teltow, Germany), and the experiment could be
initiated only if the calibration had been successfully conducted. The acceptable accuracy
for the 5-point calibration and validation was 0.5 degrees for both axes. Data validation in
the form of the visual inspection was performed immediately after each recording session,
using the BeGaze software. Each trial was adequately characterized by 3 data sequences,
one representing the
x
coordinates, the other representing the
y
coordinates of the gaze, and
the final one representing the event status signal of the recording (indicating the following
events: fixation, saccade, blink/missing data).
Sensors 2022,22, 4900 6 of 18
3.2. Data Visualization and Feature Extraction
An original visualization technique was implemented so that the gaze data could be
easier to analyze and display in a more intuitive manner. The
x
and
y
gaze coordinates of
a given trail are plotted in an x−yplane, following several rules:
•
the color of the gaze line plotted between points
pk−1=(xk−1,yk−1)
and
pk=(xk,yk)
is calculated based on the distance between points
pk−1
and
pk
, using a jet color map
(color map covers the line length range from 0 to 200 pixels, where 200 pixels is the
maximal length of a saccade in the experiment, excluding saccades that occur between
two lines of text);
•
lines that connect the gaze points that belong to fixations are connected fully, while
the lines that connect the points belonging to saccades are dashed;
•
the last recorded gaze coordinates before and after a detected blink state are marked
with red stars;
•
the opacity of the line connecting the gaze points decreases over the course of the trial
(time t) according to the following equation
opacityt=0.9 −0.8 ∗min1, t
MRT ×Ts, (1)
•
where
Ts
represents the sampling frequency (
Ts=
60
Hz)
, and the opacity ranged
from 1 (completely opaque) to 0 (completely transparent). The opacity is calculated so
that it linearly decreases over time, up to the
MRT
, which represents the maximum
reading time in this study (40 s).
An example of trial visualization is given in Figure 1.
Sensors 2022, 22, x FOR PEER REVIEW 6 of 18
initiated only if the calibration had been successfully conducted. The acceptable accuracy
for the 5-point calibration and validation was 0.5 degrees for both axes. Data validation in
the form of the visual inspection was performed immediately after each recording session,
using the BeGaze software. Each trial was adequately characterized by 3 data sequences,
one representing the 𝑥 coordinates, the other representing the 𝑦 coordinates of the gaze,
and the final one representing the event status signal of the recording (indicating the fol-
lowing events: fixation, saccade, blink/missing data).
3.2. Data Visualization and Feature Extraction
An original visualization technique was implemented so that the gaze data could be
easier to analyze and display in a more intuitive manner. The 𝑥 and 𝑦 gaze coordinates
of a given trail are plotted in an 𝑥−𝑦 plane, following several rules:
• the color of the gaze line plotted between points 𝑝 =𝑥
,𝑦 and 𝑝=
𝑥,𝑦 is calculated based on the distance between points 𝑝 and 𝑝, using a jet
color map (color map covers the line length range from 0 to 200 pixels, where 200
pixels is the maximal length of a saccade in the experiment, excluding saccades that
occur between two lines of text);
• lines that connect the gaze points that belong to fixations are connected fully, while
the lines that connect the points belonging to saccades are dashed;
• the last recorded gaze coordinates before and after a detected blink state are marked
with red stars;
• the opacity of the line connecting the gaze points decreases over the course of the
trial (time t) according to the following equation
opacit
y
= 0.9 −0.8∗ min1, t
𝑀𝑅𝑇 𝑇, (1)
• where 𝑇 represents the sampling frequency (𝑇= 60 Hz, and the opacity ranged
from 1 (completely opaque) to 0 (completely transparent). The opacity is calculated
so that it linearly decreases over time, up to the 𝑀𝑅𝑇, which represents the maxi-
mum reading time in this study (40 s).
An example of trial visualization is given in Figure 1.
Figure 1. Trial visualization example for (A) a control subject and (B) a dyslexic subject. The color
of the line represents the line length in pixels according to the presented color scale and the red
stars represent blink events.
Figure 1.
Trial visualization example for (
A
) a control subject and (
B
) a dyslexic subject. The color of
the line represents the line length in pixels according to the presented color scale and the red stars
represent blink events.
By analyzing the visualized trials, the global tendencies of the dyslexic subjects could
be observed, which were then quantified by signal features.
The nine conventional eye-tracking metrics used as features for classification were:
Fixation count, Saccade count,Fixation frequency,Saccade frequency,Fixation average duration,
Sensors 2022,22, 4900 7 of 18
Saccade average duration,Fixation total duration,Saccade total duration, and Total reading
time [42].
Aside from the commonly analyzed eye-tracking metrics, three new spatial features
were introduced, as well as two new temporal ones. The three spatial features are related
to fixation events and do not directly rely on the fixation duration or number of fixations.
Rather, they are focused on quantifying the irregularity and complexity of the gaze during
fixation events in the
x−y
coordinate plane. The first proposed spatial feature is called the
Fixation intersection coefficient (FIC), and it is calculated per trial as
FIC =1
n
n
∑
j=1
FIj(2)
where
n
represents the number of fixations in a trial, and
FIj
represents the number of
self-intersections of the lines belonging to the fixation
j
in the
x−y
plane. This feature was
introduced because a higher number of self-intersections of gaze lines during fixations was
observed in dyslexic subjects when compared to the control ones. The second spatial feature
metric is called Fixation intersection variability, and it represents the standard deviation of
the previously described
FIj
array. This was introduced because the number of self-
intersections in fixation gaze lines varied more within a single trial for dyslexic subjects
when compared to the control subjects. The third spatial feature is called the Fixation fractal
dimension (FFD), and it is calculated per trial as
FFD =1
n
n
∑
j=1
FDj(3)
where
FDj
represents the fractal dimension of the figure created by the lines belonging to
the fixation
j
in the
x−y
plane, estimated by the box-counting method [
43
]. This feature
was introduced to directly quantify the complexity of the fixation gaze lines.
In addition to the spatial features, two temporal features were introduced, named
Active reading time and Saccade variability. The Active reading time is calculated as the time
spent in the fixation and saccade states, effectively excluding the time spent in the blink
state or the intervals where the gaze was not detected. It was introduced with the goal of
observing only the time spent actively reading the displayed text. Finally, Saccade variability
was calculated as the standard deviation of the time intervals between two succeeding
saccades. This feature was introduced by focusing on the observed tendency of the saccadic
events to be more equally spaced out in the control subjects, as opposed to the
dyslexic ones
.
The data visualization and feature extraction were implemented in the Python 3.8.1
environment.
3.3. Machine Learning and Statistical Analysis
After the feature extraction, each trial was represented by a set of 14 (9 conventional
and 5 proposed) features and its appropriate label (control or dyslexic). The obtained
dataset was used to train four ML algorithms as well as to perform statistical analysis.
The selected ML algorithms were the LR, SVM, KNN, and RF. They are implemented
in the Python 3.8.1 programming language, using the sklearn library [
44
]. When training
each of the algorithms, the training set was standardized (made to have a mean value of 0
and standard deviation of 1), and the parameters for standardization on the train set were
later used on the test set. The training/hyper parameters of the models were kept at their
default values from the sklearn library (aside from the probability parameter used in the
SVM implementation) and are as follows for the used ML algorithms:
•LR: penalty = l2; C = 1; solver = lbfgs; maximum iteration number = 100;
•SVM: C = 1; kernel = rbf, probability = True;
•KNN: number of neighbors = 5; algorithm = auto; distance = Euclidian;
Sensors 2022,22, 4900 8 of 18
•
RF: number of estimators = 100; criterion for split = Gini impurity; no max depth; max
features = pnumb er o f f eatures; using bootstrap.
Each ML algorithm was trained and evaluated for each individual feature (1 input);
for the conventional features (9 inputs); for the proposed features (5 inputs); and for all
the features (14 inputs). The summary of all 17 possible input options for each of the ML
algorithms is given in Table 1. Each of the algorithm and input feature combinations was
evaluated using a subject-wise leave-one-out cross-validation, where the trial data from
each subject belonged to a single fold (30 folds in total), and in each iteration, one fold
was used for testing and the remaining ones for training. The prediction value, label, and
prediction probability for each instance of a test fold were saved and concatenated so that
after the cross-validation was finished, the evaluation metrics (accuracy, ACC; sensitivity,
Se; specificity, Sp; F1 score; area under the receiver operating characteristic curve, AUROC)
could be calculated on the entirety of the test folds.
Table 1. ML algorithm input feature options.
Algorithm Input Options
No. Feature Set Input Options No. Single Feature Input (1 Input)
1.
Conventional features (9 inputs):Fixation count, Fixation total
duration, Fixation frequency, Fixation average duration, Saccade
count, Saccade total duration, Saccade frequency, Saccade average
duration, Total reading time
4. Active reading time
5. Fixation intersection coefficient
6. Saccade variability
7. Fixation intersection variability
2.
Proposed features (5 inputs):Active reading time, Fixation
intersection coefficient, Saccade variability, Fixation intersection
variability, Fixation fractal dimension
8. Fixation fractal dimension
9. Fixation count
10. Fixation total duration
11. Fixation frequency
12. Fixation average duration
13. Saccade count
14. Saccade total duration
15. Saccade frequency
3. Conventional and Proposed features (14 inputs) 16. Saccade average duration
17. Total reading time
Other forms of ML evaluation, such as stratified 5-fold or stratified 3-fold subject-
wise evaluations, were attempted (a single fold having 30/5 = 6 or 30/3 = 10 subjects,
respectively) but showed a negligible difference in terms of the evaluation metrics when
compared to the leave-one-out method.
Feature ranking was performed in order to sort the features in terms of their impor-
tance with regard to dyslexia classification, and it was based on the decrease in impurity
in the RF algorithm [45]. The statistical analysis was then performed in the SPSS software
(16.0, IBM Corp., New York, NY, USA) for each of the features that were shown to be
indicative of dyslexic behavior by the feature ranking. First, the Mann–Whitney test was
performed to compare the feature values for each color configuration separately between
the two subject groups (dyslexic and control). Second, the Levene test of homogeneity of
variances was performed with the goal of comparing the dispersity of the observed feature
for each color configuration separately, between the two subject groups. The final part of
the statistical analysis included a Wilcoxon signed ranks test performed within the dyslexic
subject group, comparing the feature values between different color configurations. This
analysis was performed for each pair of color configurations to determine whether a given
color configuration was more favorable for the dyslexic subjects.
The analysis pipeline performed in this paper is given in Figure 2.
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configurations. This analysis was performed for each pair of color configurations to
determine whether a given color configuration was more favorable for the dyslexic
subjects.
The analysis pipeline performed in this paper is given in Figure 2.
Figure 2. The analysis pipeline. LR—logistic regression; SVM—support vector machine; KNN—k-
nearest neighbors; RF—random forest.
4. Results
The average metrics achieved on the test sets for the four ML algorithms (LR, SVM,
KNN, RF), using three different feature sets (conventional, proposed, and all features) as
inputs, are given in Table 2.
Table 2. Feature group classification evaluation metrics (the proposed feature results marked with
bold text). ACC—accuracy; Se—sensitivity; Sp—specificity; AUROC—area under the receiver
operating characteristic curve.
Feature Group ML Algorithm
LR SVM KNN RF
Conventional features
ACC 0.84 0.85 0.81 0.82
Se 0.78 0.72 0.66 0.75
Sp 0.90 0.97 0.94 0.92
F1 score 0.83 0.82 0.77 0.81
AUROC 0.88 0.89 0.87 0.86
Proposed features
Figure 2.
The analysis pipeline. LR—logistic regression; SVM—support vector machine; KNN—k-
nearest neighbors; RF—random forest.
4. Results
The average metrics achieved on the test sets for the four ML algorithms (LR, SVM,
KNN, RF), using three different feature sets (conventional, proposed, and all features) as
inputs, are given in Table 2.
The achieved results show an overall high accuracy and a consistently better result
when using the proposed features as well as the all features as inputs in comparison to the
conventional ones. The best accuracy for both the proposed features as inputs and the all
features as inputs was obtained by the LR algorithm, and it convincingly surpassed the
best accuracy of 85% obtained for the conventional features by the SVM algorithm.
The average test set accuracy achieved when each individual feature is used as the
ML input is shown in Table 3. The other metrics for single feature evaluation are presented
in Appendix A.
The best accuracy was achieved for the Fixation intersection variability feature. The
second and third best accuracies were achieved for the Fixation intersection coefficient and
the Fixation fractal dimension. The accuracies achieved for these three features for all the
ML algorithms were higher than the accuracies achieved when using all the conventional
features as inputs.
The importance of each individual feature was also ranked using the decrease in
impurity in the RF algorithm [45], and the results are shown in Figure 3.
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Table 2.
Feature group classification evaluation metrics (the proposed feature results marked with
bold text). ACC—accuracy; Se—sensitivity; Sp—specificity; AUROC—area under the receiver
operating characteristic curve.
Feature Group ML Algorithm
LR SVM KNN RF
Conventional features
ACC 0.84 0.85 0.81 0.82
Se 0.78 0.72 0.66 0.75
Sp 0.90 0.97 0.94 0.92
F1 score 0.83 0.82 0.77 0.81
AUROC 0.88 0.89 0.87 0.86
Proposed features
ACC 0.94 0.93 0.88 0.93
Se 0.89 0.88 0.78 0.89
Sp 0.98 0.98 0.98 0.97
F1 score 0.93 0.93 0.86 0.93
AUROC 0.96 0.98 0.94 0.95
All features
ACC 0.94 0.93 0.87 0.94
Se 0.89 0.87 0.75 0.86
Sp 0.98 0.98 0.98 0.97
F1 score 0.93 0.92 0.84 0.91
AUROC 0.96 0.97 0.94 0.94
Table 3. Classification accuracies for single feature inputs.
Feature
ML Algorithm
SVM LR RF KNN
Proposed
Active reading time 0.78 0.75 0.74 0.76
Fixation intersection coefficient 0.90 0.90 0.89 0.89
Saccade variability 0.74 0.74 0.76 0.73
Fixation intersection variability 0.91 0.90 0.91 0.91
Fixation fractal dimension 0.89 0.90 0.89 0.89
Conventional
Fixation count 0.84 0.85 0.84 0.84
Fixation total duration 0.78 0.74 0.77 0.76
Fixation frequency 0.35 0.30 0.52 0.63
Fixation average duration 0.46 0.49 0.48 0.63
Saccade count 0.81 0.81 0.83 0.82
Saccade total duration 0.78 0.74 0.76 0.76
Saccade frequency 0.57 0.47 0.63 0.57
Saccade average duration 0.48 0.56 0.60 0.56
Total reading time 0.80 0.77 0.74 0.75
The feature importance ranking indicates that the three proposed spatial features
(Fixation intersection coefficient,Fixation fractal dimension, and Fixation intersection variability)
that achieved the highest individual accuracy do indeed contribute to a high classification
accuracy when observed as part of a feature set. Considering this, the three proposed
features were used for further statistical analysis.
The boxplots of the Fixation intersection coefficient,Fixation fractal dimension, and Fixation
intersection variability for each color configuration and each subject group (dyslexic and
control) are shown in Figure 4.
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Figure 3. Feature importance of the eye-tracking features based on the decrease in the impurity of
the random forest algorithm.
The feature importance ranking indicates that the three proposed spatial features
(Fixation intersection coefficient, Fixation fractal dimension, and Fixation intersection variability)
that achieved the highest individual accuracy do indeed contribute to a high classification
accuracy when observed as part of a feature set. Considering this, the three proposed
features were used for further statistical analysis.
The boxplots of the Fixation intersection coefficient, Fixation fractal dimension, and
Fixation intersection variability for each color configuration and each subject group (dyslexic
and control) are shown in Figure 4.
Figure 3.
Feature importance of the eye-tracking features based on the decrease in the impurity of
the random forest algorithm.
The boxplots show that there is a clear difference between the dyslexic and control
classes for each color configuration (the control group has much lower feature values than
the dyslexic group). This was further proved by the statistical analysis. For the three
most important features, for each color configuration, a statistically significant difference
was achieved between the subject classes (p< 0.001) using the Mann–Whitney test. Fur-
thermore, the Levene test of the dispersity between the subject groups also showed a
statistically significant difference for each of the three fixation complexity features (Fixation
intersection coefficient,Fixation fractal dimension,Fixation intersection variability) for every
color configuration (p< 0.01). The Mann–Whitney test shows that the feature values sig-
nificantly differ between the groups, and the Levene test of dispersity shows that for each
color configuration, the dyslexic group has many more dispersed data points than the
control group.
In order to determine whether there was a color that had a more positive influence
on dyslexic subjects (the color that would produce the lowest feature values, as close as
possible to the values of the control group), a statistical analysis was performed within the
dyslexic subject group, comparing each pair of color configurations. The Wilcoxon signed
ranks test showed that there was a statistically significant difference (p< 0.01) only for
three pairs of color configurations and only for a single feature (Fixation fractal dimension):
(1) yellow overlay and orange overlay, (2) orange background and yellow background, and
(3) turquoise background and yellow background. The visualization of the configuration
pairs for which there was a statistically significant difference, as well as for three arbitrary
configurations for which there was no significant difference, is shown in Figure 5.
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Figure 4. The boxplots of (A) Fixation intersection coefficient, (B) Fixation fractal dimension, (C) Fixation
intersection variability for each color configuration and two subject groups (dyslexic and control).
Figure 4.
The boxplots of (
A
)Fixation intersection coefficient, (
B
)Fixation fractal dimension, (
C
)Fixation
intersection variability for each color configuration and two subject groups (dyslexic and control).
Sensors 2022,22, 4900 13 of 18
Sensors 2022, 22, x FOR PEER REVIEW 13 of 18
The boxplots show that there is a clear difference between the dyslexic and control
classes for each color configuration (the control group has much lower feature values than
the dyslexic group). This was further proved by the statistical analysis. For the three most
important features, for each color configuration, a statistically significant difference was
achieved between the subject classes (p < 0.001) using the Mann–Whitney test.
Furthermore, the Levene test of the dispersity between the subject groups also showed a
statistically significant difference for each of the three fixation complexity features
(Fixation intersection coefficient, Fixation fractal dimension, Fixation intersection variability) for
every color configuration (p < 0.01). The Mann–Whitney test shows that the feature values
significantly differ between the groups, and the Levene test of dispersity shows that for
each color configuration, the dyslexic group has many more dispersed data points than
the control group.
In order to determine whether there was a color that had a more positive influence
on dyslexic subjects (the color that would produce the lowest feature values, as close as
possible to the values of the control group), a statistical analysis was performed within
the dyslexic subject group, comparing each pair of color configurations. The Wilcoxon
signed ranks test showed that there was a statistically significant difference (p < 0.01) only
for three pairs of color configurations and only for a single feature (Fixation fractal
dimension): (1) yellow overlay and orange overlay, (2) orange background and yellow
background, and (3) turquoise background and yellow background. The visualization of
the configuration pairs for which there was a statistically significant difference, as well as
for three arbitrary configurations for which there was no significant difference, is shown
in Figure 5.
Figure 5. The visualization of data for all dyslexic subjects, for the three color configurations that
(A) show a statistically significant difference and (B) show no statistical difference. Dots represent
the background color configurations, and circles represent the overlay color configurations.
5. Discussion
In this paper, several ML algorithms and statistical tests were performed with the
goal of analyzing the dyslexic tendencies in a group of 30 children (15 dyslexic and 15
control). The text was written in the subjects’ native language, Serbian, which has a perfect
matching between letters and phonemes. Considering dyslexia detection in such
languages (the ones with a shallow orthographic system) is often quite difficult; an
Figure 5.
The visualization of data for all dyslexic subjects, for the three color configurations that
(
A
) show a statistically significant difference and (
B
) show no statistical difference. Dots represent the
background color configurations, and circles represent the overlay color configurations.
5. Discussion
In this paper, several ML algorithms and statistical tests were performed with the
goal of analyzing the dyslexic tendencies in a group of 30 children (15 dyslexic and
15 control). The text was written in the subjects’ native language, Serbian, which has
a perfect matching between letters and phonemes. Considering dyslexia detection in
such languages (the ones with a shallow orthographic system) is often quite difficult;
an accuracy of 94% achieved on the balanced dataset used in this paper (F1 score 0.93
and AUROC 0.96) (Table 2) shows a promising result that is comparable to the ones
achieved in the literature
[29,30,32,33,35–37,39–41]
which were performed on languages
with deeper orthographic systems. As the Serbian language has a shallow orthographic
system, making dyslexia harder to diagnose, we consider the observed subject pool rele-
vant for the performed research purposes for a language such as Serbian. Although the
number of participants used in this study is lower than the subject groups found in the
literature [
28
,
29
,
36
,
39
,
40
], the number of total used trials (378 trials, explained in Section 3.1)
provided enough data for the performed type of machine learning analysis.
The three most important features (Fixation intersection coefficient,Fixation fractal dimen-
sion, and Fixation intersection variability, Figure 3) that describe the fixation gaze complexity
achieved a decently high accuracy (89% or higher, Table 3), even when they were used
as the single input feature for the ML algorithms. The importance of feature design and
data interpretation has shown to be quite significant as a single spatial feature describing
fixation gaze complexity achieved a better accuracy (91% for Fixation intersection coefficient)
than all of the observed conventional features combined (85%). It is important to note
that the fixation complexity features clearly have lower values for the control subjects and
higher values for the dyslexic ones. The fixation complexity features, and consequently the
gaze pattern complexity, could therefore be considered an indication of reading difficulties
that can be observed in dyslexic subjects.
The proposed features should also be of use in dyslexia analysis for languages besides
Serbian as struggling to focus on words could yield similar chaotic fixation movements in
other languages. The drawback of the features is that they do require a certain sampling
frequency and eye-tracker precision as the characterization of fixations that is used in this
Sensors 2022,22, 4900 14 of 18
work does rely on detecting fine eye movements. The field of view of the reader can also
influence the quality of the feature as reading from a further/shorter distance from the
screen/paper could enable the reader to have a different number of words within a single
focus point. This can, in turn, make the chaotic movement of the gaze either harder to
detect or perhaps more saccadic, which might influence the separability of the classes.
The statistical analysis showed that the spatial features provide clear class separability
regardless of color configuration, as seen in Figure 4. The statistical differences between the
subject groups for all the color configurations show that a single color cannot be used to
make reading easier, to the degree that the dyslexic and control groups are not separable.
The comparison between color configurations for dyslexic subjects shows that there
could be color configurations that are more favorable than others. The analysis within
the dyslexic group also showed a statistically significant difference only between three
pairs of colors, as seen in Figure 5, indicating that none of the colors, universally, makes
reading easier or harder when compared to the other ones. A lack of a consistently superior
configuration, however, indicates that the colors have a different effect on each subject and
that, in order to make reading easier for children with dyslexia, an individualistic approach
would most likely be the best solution. The same conclusion could be reached by observing
the statistical analysis between subject groups, as the statistical significance was prominent
for each color configuration, indicating that none of the colors stands out in the sense of
making dyslexic and control subjects more similar in their reading patterns.
6. Conclusions
The paper introduced a novel spatiotemporal feature set for recognition of gaze pat-
terns in dyslexic native Serbian speakers. The proposed feature set has shown a significant
classification improvement in comparison to conventional eye-tracking features (94% vs.
85%). The statistical analysis between subject classes (dyslexic and control) found high
class separability, independent of color configuration. A statistical analysis related to the
color impact on reading performance was accomplished within the dyslexic subject group
and showed high inter-subject variability.
The performed study was limited by the number of participants and by the usage
of a high-precision eye tracker. However, the obtained results are promising in the field
of dyslexia detection, and further work could include an introduction of the features
measured from other sensor systems (including low-cost systems), analyzing a larger
number of subjects, or a subject base of broader age distribution. Analyzing the data from
different eye trackers and combining the obtained dataset with other datasets (possibly in
different languages) would also be of interest for future work.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/s22134900/s1, Figure S1: White background test board exam-
ple; Figure S2: Yellow background test board example; Figure S3: Red overlay test board example;
Figure S4
: Orange background test board example; Figure S5: Yellow overlay test board exam-
ple;
Figure S6
: Orange overlay test board example; Figure S7: Blue overlay test board example;
Figure S8
: Purple background test board example; Figure S9: Purple overlay test board example;
Figure S10: Red background test board example; Figure S11: Turquoise overlay test board ex-
ample; Figure S12: Blue background test board example; Figure S13: Turquoise background test
board example.
Author Contributions:
Conceptualization, I.V., V.K., T.P., A.M.S. and M.M.J.; methodology, I.V., V.K.,
A.M.S. and M.M.J.; software, I.V.; formal analysis, I.V., V.K. and M.M.J.; data acquisition, T.P. and
M.M.J.; resources, V.K., T.P. and M.M.J.; data curation, I.V., V.K., T.P., A.M.S. and M.M.J.; writing—
original draft preparation, I.V., V.K. and M.M.J.; writing—review and editing, I.V., V.K., T.P., A.M.S.
and M.M.J.; visualization, I.V.; project administration, M.M.J. All authors have read and agreed to the
published version of the manuscript.
Funding:
This research was supported by the Ministry of Education, Science and Technology Develop-
ment of Serbia, Belgrade, Serbia (contracts 451-03-68/2022-14/200103 and 451-03-68/2022-14/200223).
Sensors 2022,22, 4900 15 of 18
Institutional Review Board Statement:
The study was conducted in accordance with the Declaration
of Helsinki, and approved by the ethical committee of the Psychology Department of the Univer-
sity of Niš (a branch of the Serbian Psychology Association), experimental procedure No. 9/2019
(04.09.2019).
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Sensitivity for single feature inputs.
Feature
ML Algorithm
SVM LR RF KNN
Proposed
Active reading time 0.59 0.65 0.60 0.61
Fixation intersection coefficient 0.85 0.84 0.83 0.84
Saccade variability 0.54 0.61 0.62 0.58
Fixation intersection variability 0.85 0.82 0.85 0.85
Fixation fractal dimension 0.84 0.87 0.83 0.84
Conventional
Fixation count 0.74 0.78 0.76 0.77
Fixation total duration 0.59 0.64 0.60 0.59
Fixation frequency 0.31 0.29 0.48 0.50
Fixation average duration 0.32 0.33 0.43 0.50
Saccade count 0.74 0.75 0.82 0.83
Saccade total duration 0.59 0.64 0.61 0.59
Saccade frequency 0.44 0.46 0.42 0.45
Saccade average duration 0.28 0.46 0.44 0.44
Total reading time 0.61 0.65 0.59 0.62
Table A2. Specificity for single feature inputs.
Feature
ML Algorithm
SVM LR RF KNN
Proposed
Active reading time 0.95 0.83 0.89 0.90
Fixation intersection coefficient 0.95 0.96 0.93 0.94
Saccade variability 0.94 0.85 0.87 0.86
Fixation intersection variability 0.96 0.97 0.94 0.96
Fixation fractal dimension 0.93 0.92 0.93 0.93
Conventional
Fixation count 0.93 0.91 0.91 0.91
Fixation total duration 0.95 0.83 0.92 0.91
Fixation frequency 0.39 0.34 0.59 0.76
Fixation average duration 0.60 0.64 0.60 0.77
Saccade count 0.87 0.85 0.82 0.83
Saccade total duration 0.95 0.83 0.89 0.91
Saccade frequency 0.70 0.47 0.73 0.70
Saccade average duration 0.69 0.66 0.81 0.70
Total reading time 0.97 0.86 0.83 0.87
Table A3. F1 score for single feature inputs.
Feature
ML Algorithm
SVM LR RF KNN
Proposed
Active reading time 0.72 0.71 0.70 0.71
Fixation intersection coefficient 0.89 0.89 0.87 0.88
Saccade variability 0.67 0.69 0.70 0.68
Fixation intersection variability 0.90 0.89 0.89 0.90
Fixation fractal dimension 0.88 0.89 0.87 0.88
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Table A3. Cont.
Feature
ML Algorithm
SVM LR RF KNN
Conventional
Fixation count 0.81 0.83 0.82 0.83
Fixation total duration 0.72 0.71 0.71 0.70
Fixation frequency 0.32 0.28 0.50 0.57
Fixation average duration 0.37 0.39 0.47 0.58
Saccade count 0.78 0.79 0.82 0.83
Saccade total duration 0.72 0.71 0.71 0.70
Saccade frequency 0.50 0.46 0.50 0.51
Saccade average duration 0.34 0.51 0.53 0.50
Total reading time 0.74 0.73 0.67 0.71
Table A4. Area under the receiver operating characteristic curve for single feature inputs.
Feature
ML Algorithm
SVM LR RF KNN
Proposed
Active reading time 0.67 0.79 0.73 0.75
Fixation intersection coefficient 0.94 0.95 0.92 0.94
Saccade variability 0.74 0.73 0.77 0.77
Fixation intersection variability 0.94 0.95 0.93 0.94
Fixation fractal dimension 0.93 0.96 0.92 0.94
Conventional
Fixation count 0.87 0.89 0.86 0.87
Fixation total duration 0.67 0.78 0.72 0.72
Fixation frequency 0.37 0.32 0.59 0.61
Fixation average duration 0.51 0.38 0.58 0.62
Saccade count 0.85 0.87 0.85 0.87
Saccade total duration 0.67 0.78 0.72 0.72
Saccade frequency 0.53 0.34 0.59 0.58
Saccade average duration 0.40 0.54 0.60 0.58
Total reading time 0.68 0.79 0.73 0.76
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