# ESPiM: Eye-Strain Probation Model, An Eye-Tracking Analysis Measure for
Digital Displays

Mohsen Parisay  aDepartment of Computer Science and Software Engineering
<EMAIL_ADDRESS>n_haghbi@encs.concordia.ca); bImmersive and
Creative Technologies Lab (charalambos@poullis.org); cPERFORM Center
(marta@ap-lab.ca); Concordia University, Montreal, Canada  Negar Haghbina
aDepartment of Computer Science and Software Engineering
<EMAIL_ADDRESS>n_haghbi@encs.concordia.ca); bImmersive and
Creative Technologies Lab (charalambos@poullis.org); cPERFORM Center
(marta@ap-lab.ca); Concordia University, Montreal, Canada  Charalambos
Poullisa,b aDepartment of Computer Science and Software Engineering
<EMAIL_ADDRESS>n_haghbi@encs.concordia.ca); bImmersive and
Creative Technologies Lab (charalambos@poullis.org); cPERFORM Center
(marta@ap-lab.ca); Concordia University, Montreal, Canada  and Marta Kersten-
Oertela,c aDepartment of Computer Science and Software Engineering
<EMAIL_ADDRESS>n_haghbi@encs.concordia.ca); bImmersive and
Creative Technologies Lab (charalambos@poullis.org); cPERFORM Center
(marta@ap-lab.ca); Concordia University, Montreal, Canada

###### Abstract

Eye-strain is a common issue among computer users due to the prolonged periods
they spend working in front of digital displays. This can lead to vision
problems, such as irritation and tiredness of the eyes and headaches. We
propose the Eye-Strain Probation Model (ESPiM), a computational model based on
eye-tracking data that measures eye-strain on digital displays based on the
spatial properties of the user interface and display area for a required
period of time. As well as measuring eye-strain, ESPiM can be applied to
compare (a) different user interface designs, (b) different display devices,
and (c) different interaction techniques. Two user studies were conducted to
evaluate the effectiveness of ESPiM. The first was conducted in the form of an
in-person study with an infrared eye-tracking sensor with 32 participants. The
second was conducted in the form of an online study with a video-based eye-
tracking technique via webcams on users’ computers with 13 participants. Our
analysis showed significantly different eye-strain patterns based on the video
gameplay frequency of participants. Further, we found distinctive patterns
among users on a regular 9-to-5 routine versus those with more flexible work
hours in terms of (a) error rates and (b) reported eye-strain symptoms.

#### Keywords

Eye-strain, eye-tracking analysis, gaze-based interaction, Fitts’ Law

##  1 Introduction

Today, in many parts of the world, many employees work sitting in front of
computers for eight hours a day. The typical work atmosphere, which includes
working in enclosed spaces, e.g. cubicles or offices, and dealing with
multiple devices (e.g. PC, smartphone, telephone) simultaneously (i.e.
multitasking), can lead to an increased workload. Yet, there is a strong
correlation between excessive screen time and health risks such as
cardiovascular diseases, impaired vision, and bone density [21] and a
correlation between long screen times of elementary school students with dry
eyes (the malfunctioning of tear production in the eyes [39]) and learning
abilities [29].

Computer vision syndrome (CVS) is composed of multiple eye vision problems due
to the prolonged use of digital displays, including tablets and smartphones.
Other symptoms of CVS include eye-strain, eye burn, double vision, and blurred
vision [31]. These CVS symptoms not only affect visual comfort but also are a
major cause of work-related stress [32] and productivity in both adults and
teenagers [37]. In this paper, we address eye-strain, also known as visual
fatigue, as one of the significant CVS symptoms [31]. Eye-strain can be caused
by moving images [14] and occurs when focusing on near objects. It is a common
issue among computer users due to the prolonged periods they spend working in
front of a monitor [41]. Eye-strain can cause motion sickness [19], vision
problems such as irritation and tiredness of the eyes, and headaches [40].

In this paper, we introduce the Eye-Strain Probation Model (ESPiM), an
integrated measurement model for eye-strain based on target selection tasks
relying on spatial targets’ and screen properties via Fitts’ Law and eye-
tracking fixation points.

To demonstrate the efficacy of ESPiM, we measured target selection performance
with data from two eye-tracking user studies, one in-person laboratory study
and one online web application study. We considered the distinctive patterns
among participants based on video gameplay frequency. Moreover, we studied
eye-strain patterns, including eye symptoms among typical 9-to-5 participants
and flexible (anytime beyond 9-to-5) participants and found the flexible
groups experienced a higher number of eye symptoms than the 9-to-5 group.
Moreover, we recorded significantly higher error rates for the 9-to-5 groups
than the flexible group.

##  2 Related Work

In the following section, we describe Fitts’ Law which is related to task
difficulty of target selection tasks and used within the ESPiM model, as well
as previous work in eye-strain and eye-tracking models.

###  2.1 Fitts’ Law

Originally proposed to measure task difficulty, Fitts’ Law predicts the amount
of movement time (MT) to activate a target based on a specific size (W) at a
certain distance (D). Fitts’ Law is formulated as:
$MT=a+b\log_{2}(\frac{2D}{W})$, where a and b are empirically defined constant
values. However, the Shannon formulation is most commonly used to calculate
the index of difficulty in the field of HCI, $ID=\log_{2}(1+\frac{D}{W})$
[23]. Among the earliest applications in HCI, Fitts’ Law was used to compare
input devices (mouse, joystick, step keys and text keys) for text selection on
a display [5]. Fitts’ Law has also been applied effectively in various studies
to analyze the performance of selecting targets (e.g. [8], [16]). Many
extensions of the original Fitts’ Law have been proposed for different case
scenarios. For instance, MacKenzie _et al._ proposed an extension from a one-
dimension to a 2D model for target acquisition tasks enabling the improvement
of the index of difficulty for interactive computer systems with higher
accuracy [25].

Fitts’ Law is a popular tool for user studies that has been extensively
applied in evaluations [11]. For example, Kim et al. analyzed the usability of
touch-key sizes in a driving safety simulation [17]. Hansen et al. studied the
performance of gaze and head tracking for point and selection tasks on head-
mounted displays (HMDs) [12]. In addition, researchers applied Fitts’ Law to
reduce dwell-time for gaze-based interactions by taking into account the
estimated target acquisition time and eye movement time [13].

###  2.2 Eye-strain Models

Researchers have proposed various means to measure eye-strain based on eye
movement analysis. Lanthier et al. showed that eye fixations and eye-strain
increases with fatigue [20]. Komogortsev _et al._ proposed the Fixation
Quantitative Score (FQnS) to consider the number of fixation points in regards
to a stimulus, and Fixation Qualitative Score (FQlS) based on distance drift
of ﬁxation points that may reveal physical eye-strain [18]. Furthermore,
Vasiljevas et al. adopted an analytical model for muscle fatigue to assess
eye-strain in gaze-based tasks [41]. Researchers have also applied self-
evaluation rating questionnaires to measure eye-strain for gaze-based
applications [26]. Further, saccades-based approaches were proposed to measure
eye-strain [4, 28, 9]. However, analysis of saccades might raise challenges
and need extra processing on budget-friendly devices, and therefore fixation-
based approaches have been preferred [1].

Considering the previous works, we propose a dual-purpose approach that can be
applied in user studies with eye trackers to measure eye-strain based on
screen and target properties for a specific duration. In our previous works,
we proposed one approach to measure eye-strain involving subjective ratings
(FELiX) [34] and introduced an index of difficulty for eye-tracking
applications (IDEA) [30]. These approaches were compound models based on
subjective and objective measures. The introduction of ESPiM is based on
objective measures only, which may improve and optimize user interface design
concepts based on eye-strain criteria. Based on the results of our previous
models, we propose a predictable objective model suitable for assessments of
visual prototypes on digital displays being used for a specific time to reduce
costs of productions by comparing design ideas in early steps via pilot
studies.

##  3 Eye-Strain Probation Model (ESPiM)

The ESPiM model relies on spatiotemporal parameters related to the screen
size, target dimensions and distances (spatial) shown in Figure 2, task
duration time (temporal), and eye-tracking fixations as described below:

  1. 1.

x: width of the screen in pixels.
$x\in\mathbb{R}\land x>0$

  2. 2.

y: height of the screen in pixels.
$y\in\mathbb{R}\land y>0$

  3. 3.

z: diagonal length of the screen in pixels.
$z=\sqrt{x^{2}+y^{2}}\land z\in\mathbb{R}\land z>0$

  4. 4.

Area of screen (AoS): the surface area of the screen in which test
applications are executed on in pixels.
$AoS=x\times y\land AoS\in\mathbb{R}\land AoS>0$

  5. 5.

Area of target (AoT): the surface area of the target (user interface element)
in which the user tries to focus on in pixels. Typically there are multiple
targets on a user interface. We calculate the area of a single target since
users focus on one target at selection time. In the case of targets with
various areas, the average of all targets will be considered.
$AoT\in\mathbb{R}\land 0<AoT\leq AoS$

  6. 6.

Distance of target (D): distance of the target centers from each other in
pixels which must be smaller than or equal to screen’s diagonal length.
$D\in\mathbb{R}\land 0<D\leq z$

  7. 7.

Width of target (W): width of target on the screen in pixels, which must be
smaller than or equal to screen width.
$W\in\mathbb{R}\land 0<W\leq x$

  8. 8.

Shannon code ($\log_{2}(1+\frac{D}{W})$): index of difficulty for point-and-
select tasks [23] based on the Fitts’ Law [10].
$\log_{2}(1+\frac{D}{W})\in\mathbb{R}\land\log_{2}(1+\frac{D}{W})>0$

  9. 9.

Task duration (TD): time spent on a specific task.
$TD\in\mathbb{R}\land TD>0$

  10. 10.

Average number of fixations (ANF): average number of fixations recorded by an
eye-tracking sensor of an entire task.
$ANF\in\mathbb{R}\land ANF>0$

ESPiM: the calculated eye-strain value is based on Equation 1 which is greater
than 0 based on the square root function growth: ($ESPiM\in\mathbb{R}\land
ESPiM>0$).

The ESPiM model is based on pure test conditions such as screen and target
properties regarding the dimensions and distances to be measured for a desired
duration of time. This model provides an initial assessment to researchers
about task difficulties. The ESPiM model can be used considering any 2D flat
display type such as smartphones, tablets, and laptop/desktop monitors. This
enables researchers to predict the difficulty level of target selection tasks
on any device regardless of the applied interaction techniques.

The ESPiM model given in Equation 1 reflects the level of difficulty given the
size and distances of targets over the screen ($\log_{2}(1+\frac{D}{W})$), to
select a portion of the screen covered by a target ($\frac{AoS}{AoT}$),
considering an amount of eye fixation points to focus on targets ($ANF$) for
the specific period of time (TD). The equation is offset by 1 in the case of
minimal values for the parameters described earlier. The purpose of this
addition is to set the minimum threshold of the square root function to start
from $\sqrt{\frac{\epsilon+1}{\lambda+1}}$, where $\epsilon$ and $\lambda$
denote very small values. The average number of fixations (ANF) is one of the
most used eye-tracking variables, which contributes to the accuracy of the
calculations.

These parameters are bound into the square root function to (1) shape the
equation outcome into continuous values that are suitable for machine learning
algorithms, and (2) downscale possible large numbers due to multiplications of
parameters. Since the time unit of denominator is integrated into the equation
under the square root function, it may be ignored, and therefore we assign the
unit of bits for the ESPiM model for simplicity of comparisons. Figure 1 shows
a 3D visualization of the ESPiM model calculated for sample generated values.

$ESPiM=\sqrt{\frac{\big{(}~{}~{}\overbrace{~{}~{}(\frac{AoS}{AoT})\times\log_{2}(1+\frac{D}{W}}^{spatial})~{}~{}\times\overbrace{ANF}^{eye-
tracking}\big{)}+~{}~{}1}{\underbrace{TD}_{temporal}+1}}$ (1)

Figure 1: A 3D visualization of the ESPiM model for sample generated values.
The spatial parameter axis represents the product of the relative area ratio
of the screen over target, and the Shannon code as described in Equation 1
with a constant average number of fixations (ANF).

###  3.1 Applications of ESPiM

Although we have primarily designed the ESPiM model to measure eye-strain on
digital displays, it can be applied to (a) compare user interface designs, (b)
compare different display devices, and (c) compare different interaction
techniques based on eye-strain of users. As the spatial parameters are
included in the ESPiM model, it can be used to estimate eye-strain before
testing in a user study which can be beneficial to both research communities
and industrial producers of digital content.

In fact, we incorporate spatial parameters of screen and targets including
applying Fitts’ Law and eye-tracking fixation points for a desired period in a
single measure. Since eye fixations are typically bound to a specific range
(200-600 ms) [27], the average number of fixations (ANF) for a desired time
can be estimated. This property makes ESPiM a suitable choice for testing and
evaluating interfaces even when there is no access to eye-tracking sensors to
record eye fixations, since this parameter can be estimated based on the
finite range of ANF for a desired test duration. ESPiM can be considered as an
extension to the Fitts’ Law regarding eye-tracking case scenarios.

##  4 User Study 1: Fitts’ Study (in-person)

We conducted two user studies to evaluate the ESPiM model using (1) an in-
person infrared desktop eye tracker study and (2) a video-based eye-tracking
remote study. The purpose of the first evaluation was to study the ESPiM
model. In this study, we used the unpublished datasets of our previous paper
EyeTAP [35] in which we collected large amounts of infrared eye-tracking data.

###  4.1 Methods

During this study, participants (19 Male, 13 Female) with an average age of
25.96 years ($SE=0.53$ years), were asked to select circular targets using eye
gaze, and specifically the dwell-time method. We used the ‘FittsStudy’ V4.2.7
application [42] for our user study, which enabled us to run experiments based
on Fitts’ Law. Our stimuli contain two target widths (96 and 128 pixels) at
three distances apart (256, 384 and 512 pixels) to record the required
measures. An activation of 500 milliseconds for dwell-time was used as this is
the best-suited threshold in previous studies [24, 38]. Thus, when a
participant focuses for 500 milliseconds on a target, it triggers a click
event, and the target gets selected, and any gaze movement from the target
borders causes pointer movement and therefore restarts the target selection
process.

The user study took 12 minutes on average for each participant, 10 minutes for
preparation, including description of the task, training and eye calibration,
and 2 minutes for the actual target selection task in 6 trials (2 widths
$\times$ 3 distances). Target selection using an eye-tracking sensor with a
low activation threshold (500 ms) challenges the control of the pointer on the
screen. Conventional target selection input devices such as keyboard and mouse
enable users to select targets at their pace. Considering the differences
between these selection methods, the relatively short duration of target
selection in our study using eye-tracking was sufficient to record required
measures as well as not to overwhelm participants with heavy tasks.

To capture the eye-tracking data, a remote eye-tracking sensor (Tobii 4C) with
a sampling rate of 90 Hz on a monitor with the resolution of $1920\times 1080$
pixels (24′′) running on an Intel i7 Windows 10 PC was used. Subjects sat with
a distance of 60 cm ($\approx$23.5 in) to the eye tracker. The following data
was collected during the study: (1) movement time based on the Fitts’ Law, (2)
recorded errors of target selections, (3) average number of fixations (ANF),
and (4) Fixation Qualitative Score (FQlS) (a measure that can reveal physical
eye fatigue based on distance drift of ﬁxation points) [18]. These measures
accompanied by ESPiM calculations based on Equation 1 were needed for our
analysis.

We informed participants about the procedures of the studies, and registered
their consents before conducting the experiments.

Figure 2: Screenshot of the ‘FittsStudy’ application [42] with 3 targets. The
blue target represents the active target to be selected. The distance between
target centers is shown with D, and the width of targets is shown with W.

###  4.2 Results and Discussion

The results of our experiments were analyzed using the JASP software [15]. We
analyzed our raw data based on three categories: (1) model analysis regarding
the effectiveness of ESPiM, and (2) participants’ frequency of video gameplay.
Figure 4 illustrates the calculated ESPiM values and the recorded measures.
ESPiM can be applied to compare the results of different groups in user
studies. We analyze these basic results among groups of video gameplay
frequency.

We found a strong correlation (Pearson’s r = 0.81, and p $<$ .001) between the
measured ESPiM values and the recorded errors during the user study. This
suggests a higher error rate for higher eye-strain values as illustrated in
Figure 3.

Figure 3: Correlation of errors and ESPiM (Pearson’s r = 0.81, p $<$ .001).

Figure 4: Illustration of (a) ESPiM, (b) calculated movement time based on
Fitts’ Law, (c) recorded errors, (d) eye fixations, and (e) Fixation
Qualitative Score (FQlS) measure for 32 participants observed in our study.

####  4.2.1 Video-gameplay Analysis

In a pre-test questionnaire, we asked the participants to rate their video
gameplay frequency on an integer scale of 1 (never) to 5 (every day). We found
a negative correlation (Pearson’s r = -0.32, and p $>$ .05) which was not
statistically significant, although it shows a trend for further discussion as
shown in Figure 5. In order to further analyze the result, we divided our
subjects into two groups based on their rate regarding the mean (2.5) of the
scale. Participants with a rate lower than the average value are labeled as
low and those with a rate greater or equal than the average as high groups.

Each group was assigned exactly 16 participants. In general, we found
significantly higher eye-strain level, movement time, and error rates for the
low group compared to the high group, while no significantly different values
for the eye fixations and the FQlS measures were recorded.

Figure 5: Correlation of video game frequency and the calculated ESPiM values
(Pearson’s $r=-.32$).

Eye-strain Probation Model (ESPiM): We applied a paired-sample t-test to check
the effect of video gameplay frequency on the calculated ESPiM
($t(15)=2.14,\textit{p}<.05$) and found a significant difference between low
($M=59.52~{}bits$, $SE=0.81~{}bits$) and high ($M=57.04~{}bits$,
$SE=0.48~{}bits$) groups as illustrated in Figure 6. Thus, those that
frequently play video games experienced a significantly lower amount of eye-
strain than the group of lower frequency of playing video games.

Movement Time: We applied a paired-sample t-test to check the effect of video
gameplay frequency on movement time based on the Fitts’ Law and found a
significant difference ($t(15)=2.36,\textit{p}<.05$) between low
($M=690.37~{}msec$, $SE=34.63~{}msec$) and high ($M=584.72~{}msec$,
$SE=21.53~{}msec$) groups as illustrated in Figure 6. Thus, those with a
higher frequency of video gameplay were able to perform the task in a shorter
amount of time than those who play video games less frequently.

Error Rates: We applied a paired-sample t-test to check the effect of video
gameplay frequency on the recorded errors and found a significant difference
($t(15)=3.62,\textit{p}<.05$) between low ($M=0.69~{}errors$,
$SE=0.09~{}errors$) and high ($M=0.27~{}errors$, $SE=0.06~{}errors$) groups as
illustrated in Figure 6. This suggests that those with a higher frequency of
video gameplay made less errors than those who do not play or play less
frequently.

Eye Fixations: We applied a paired-sample t-test to check the effect of video
gameplay frequency on eye fixations with ($t(15)=2.12,\textit{p}>.05$) and
found no significant difference between low ($M=49.53~{}fixations$,
$SE=1.93~{}fixations$) and high ($M=44.13~{}fixations$, $SE=1.51~{}fixations$)
groups as illustrated in Figure 6. Thus, gameplay frequency did not impact eye
fixations during the test.

Fixation Qualitative Score (FQlS): We applied a paired-sample t-test to check
the effect of video gameplay frequency on the calculated FQlS measure (a
measure that can reveal physical eye fatigue based on distance drift of
ﬁxation points) with ($t(15)=0.28,\textit{p}>.05$) and found no significant
difference between low ($M=416.00~{}pixels$, $SE=13.13~{}pixels$) and high
($M=411.67~{}pixels$, $SE=10.12~{}pixels$) groups as illustrated in Figure 6.
In other words, gameplay frequency did not impact fixation drift distances.

Figure 6: Illustration of (a) ESPiM ($\textit{p}<.05$), (b) movement time
based on Fitts’ Law ($\textit{p}<.05$), (c) error rates ($\textit{p}<.05$),
(d) eye fixations, and (e) Fixation Qualitative Score (FQlS)
($\textit{p}>.05$) based on video gameplay frequency of 32 participants
divided into 2 equal groups (Low, High) with 16 participants.

We found a similar pattern on higher ANF (Average Number of Fixations) measure
($IQR_{low}>IQR_{high}$, and $Range_{low}>Range_{high}$). However, there were
no significant differences among low and high groups, the relatively higher
ANF was the cause of the higher ESPiM values for the low group. This suggests
that participants with higher gameplay frequency were more experienced in
moving their eyes in a shorter time and therefore produced lower fixations and
consequently lower eye-strain based on our model.

This result may be due to the fact that video games can increase visual
abilities. Previous studies have shown the relationship between video gameplay
and eye movements, for instance, shorter saccadic reaction time in video game
players [22], the usage of video games to train visual skills [2], and to
enhance visual search in players [6]. These characteristics of frequent game
players enable them to experience lower eye-strain, as indicated by our
results.

We observed that participants with a high video gameplay frequency produced
lower errors than their counterparts in target selections as shown in Figure
6. The cause of low error rates in participants with high video game
experience is due to their fast eye movements, as shown in Figure 6. Thus
those who have more “trained” visual search through video gameplay[2] tend to
perform better with eye-tracking-based selection.

The mentioned observations based on the higher performance of experienced
participants related to video gameplay cannot be interpreted as a promotion in
favour of video games.

##  5 User Study 2: Focus Shift Simulator (online)

The second study, described here, was conducted to include a more in-depth
analysis of eye-strain characteristics using a video-based eye-tracking
technique applied via the WebGazer API [33], a recognized tool for remote eye-
tracking user studies.

###  5.1 Methods

We developed a custom web application that runs on the client-side completely
and requires no video footage to be sent to the server. The WebGazer API [33]
uses the user’s webcam to track eye movement and maps features of the eye and
positions on the screen. It begins by obtaining the participant’s consent to
use the webcam, followed by a calibration process. During this process, the
user has to click on reference points with the mouse while looking at the
cursor.

Although the eye-tracking accuracy of the WebGazer API relies on the amount of
light in the environment, we informed participants to run the application in a
well-lit environment. In addition, the WebGazer API applies a learning
mechanism from user interactions and contains a self-calibration functionality
to improve its accuracy and therefore provides a sufficient approximation of
the user’s gaze points [33].

The application was developed in Java and the server interaction with the
WebGazer API [33] was implemented in JavaScript. The AWS Elastic Beanstalk [3]
provided by Amazon Web Services was used for deploying our web application.
Figure 7 shows the workflow of the focus shift simulator user study.

Figure 7: Overview of the focus shift simulator test application using the
WebGazer API [33]. The user runs the application from a web browser, the
client-server connection is established, and the WebGazer API acquires control
of the webcam to track the user’s eyes. The results of the entire test session
is saved on the file system (F.S.) on the server.

In terms of the study, participants were asked to launch the developed web
application at 3 times in the day (from 08:00 to 12:00, 12:00 to 18:00 and
after 18:00) over 7 days (did not have to be consecutive). Prior to each trial
in the study we asked the users how many hours they have been working on or
using a digital display.

Next, the participant was prompted to click on 30 buttons that randomly
appeared on the screen one at a time. Each button has a different position and
size adjusted to the screen dimension as illustrated in Figure 8. By clicking
on the last button, the recorded raw data is sent to the server. At the end of
the trial, we asked the participant to rate their current eye-strain level on
a scale from 1 (none) to 5 (a lot).

The intuition behind the developed focus shift application is based on the
multi-tasking efforts to interact with different interactions, applications,
and events while working on a digital display. The users need to move their
focal points to react to situations. For instance, reading emails and
constantly receiving messages on a messenger application and monitoring
running applications in the background, which may interrupt the user’s
attention with prompt dialog boxes to confirm/decline certain actions are
typical tasks of an office employee.

Figure 8 illustrates the screenshot of the test application to simulate multi-
tasking events on a computer and Figure 9 shows the pattern of eye fixations
when shifting to different locations on the screen to follow stimuli and
illustrates the intuition of fixation points integration in our proposed model
(see Equation 1).

Figure 8: The screenshot of the focus shift simulator application for 10
targets. We reduced the number of targets for this screenshot to reduce
overlapping of targets for higher visibility. Targets contain ‘click’ label
and appear one by one randomly across the screen with different sizes. As soon
as user clicks on a button, the application removes the selected button and
loads the next. Figure 9: The recorded fixation points of an entire test
session with 30 targets on a laptop display. The red circles represent center
of targets, and the blue circles represent fixation points. This figure shows
the intuition of fixation points to be integrated in our eye-strain model to
be considered for a specific period of time.

This study was specifically designed to be applied remotely to comply with
physical restrictions caused by the COVID-19 pandemic [7]. Moreover, since the
study collects data from 24 hours a day from users, it could be executed
anytime and anywhere via the URL to the server to provide participants freedom
and control over their interactions with the system rather than running the
tests in a laboratory equipped with eye-tracking sensors. Therefore the
application of video-based eye-tracking was found to be the ideal choice of
design.

After analyzing the preliminary results, we found that some participants
failed to stick to the restricted time slots. This may be due to the fact that
our testing sessions collided with personal tasks and that since the start of
the COVID-19 pandemic [7], many workers began working from home with varying
work routines rather than the traditional 9-to-5 workday. As we originally
wanted to study our ESPiM model for classical 9-to-5 working hours, based on
our data, we defined two groups of participants:

  * •

9-to-5: any time between 09:00 and 17:00 o’clock.

  * •

Flexible: any times not in the 9-to-5 group.

These slots were chosen based on the concept of 9-to-5 working hours, and
there are no intersections between the groups (9-to-5 $\cap$ Flexible =
$\emptyset$). We had four hypotheses that we considered in this study:

  * •

H1: Users perceive higher eye-strain level beyond the standard 9-to-5 working
hours: ESPiM(9-to-5) $<$ ESPiM(Flexible).

  * •

H2: The group of 9-to-5 participants may cause more errors than the flexible
group. In simple words, we assume that the flexible group may choose their
preferred hours of work or take more time in between work periods and thus
make fewer errors.

  * •

H3: The longer users spend on a digital display, the more eye-strain they
have.

  * •

H4: The increase of the calculated ESPiM value correlates with increased mouse
pointer movements.

We collected 70 samples from 13 participants (10 Male, 3 Female) who partook
in both experiments with an average age of 31.33 years ($SE=2.01$ years) to be
analyzed based on their working times and ratings. Specifically, we collected
(a) screen resolution, (b) test duration, (c) errors, (d) eye fixations, (e)
display hours, (f) perceived eye-strain rating, and (g) eye-strain symptoms of
participants.

We informed participants about the procedures of the studies, and registered
their consents before conducting the experiments.

###  5.2 Results and Discussion

The results of our experiments were analyzed using the JASP software [15].
Since the start of the COVID-19 pandemic [7] many knowledge workers and
students were forced to work from home, which typically deviates from the
routine of 9-to-5 working hour schedules. This phenomenon motivated us to
apply ESPiM to study the impacts of remote working via measuring eye-strain
based on our proposed equation (see Equation 1).

Although the calculated ESPiM values of both groups (9-to-5 and flexible)
showed no significant difference, we observed significant patterns in the (a)
subjective ratings, (b) time spent on a digital display, and (c) recorded
error rates among groups.

To test our first hypothesis, we applied a paired-samples t-test to check the
difference of the calculated ESPiM (t(34)=1.90, p $>$ .05) among both groups
and found no significant difference between 9-to-5 ($M=30.58~{}bits$,
$SE=1.22~{}bits$) and flexible ($M=27.76~{}bits$, $SE=1.34~{}bits$) groups as
illustrated in Figure 10. This suggests our first hypothesis (H1) concerning a
lower eye-strain level for 9-to-5 participants than the flexible group is
rejected.

Figure 10: Illustration of (a) ESPiM (p $>$ .05), (b) subjective ratings of
perceived eye-strain level (p $<$ .001), and (c) display hours before test (p
$<$ .001) of both testing groups, (d) mouse pointer movements of both testing
groups (p $>$ .05), (e) recorded eye fixation points (p $>$ .05), and (f)
error rates (p $<$ .05).

To test our second hypothesis, we applied a paired-samples t-test to check the
effect of errors on the calculated ESPiM (t(34)=2.47, p $<$ .05) and found a
significant difference between 9-to-5 ($M=0.31~{}errors$, $SE=0.09~{}errors$)
and flexible ($M=0.08~{}errors$, $SE=0.04~{}errors$) groups as illustrated in
Figure 10. As we had predicted, there were higher error rates of the 9-to-5
group compared to the flexible group in our second hypothesis (H2), which is
thus confirmed by these results. This shows that the flexible group, who are
more flexible to work on their digital devices, could finish the test with
fewer mistakes.

We applied a paired-samples t-test to check the effect of subjective ratings
on the calculated ESPiM (t(34)=3.58, p $<$ .001) and found a significant
difference between 9-to-5 ($M=1.34~{}points$, $SE=0.10~{}points$) and flexible
($M=2.00~{}points$, $SE=0.15~{}points$) groups as illustrated in Figure 10.

Further, we looked at the effect of display hours on the calculated ESPiM
(t(34)=6.52, p $<$ .001) and found a significant difference between 9-to-5
($M=2.41~{}hours$, $SE=0.27~{}hours$) and flexible ($M=5.64~{}hours$,
$SE=0.37~{}hours$) groups as illustrated in Figure 10.

Thus, although we predicted a higher eye-strain level based on ESPiM values
for participants who spend more time on a digital display in our third
hypothesis (H3), and we recorded significantly higher working hours for the
flexible group than the 9-to-5 participants, the difference of ESPiM values
was not statically significant and therefore we reject our third hypothesis.
We posit that the reason for higher working hours but relatively similar eye-
strain level of the flexible group lies in the fact that flexible participants
could work anytime based on their convenience and therefore were benefited
from breaks rather that those bound to a certain time window as the 9-to-5
group.

To test our fourth hypothesis, we studied the correlation of ESPiM and mouse
pointer movements for each group and found positive correlations among those
measures as predicted and therefore confirm the hypothesis (H4) as shown in
Figure 11, and 12. This result suggests that tired eyes may lead to an
increase of mouse pointer movements among users for target selection tasks.

In addition, we studied the effect of mouse pointer movements (t(34)=1.81, p
$>$ .05) and found no significant difference between 9-to-5
($M=667.14~{}movements$, $SE=47.87~{}movements$) and flexible
($M=565.11~{}movements$, $SE=45.04~{}movements$) groups as illustrated in
Figure 10. However, the pointer movements correlate with the calculated ESPiM
of each group. This shows that both groups of users applied a similar amount
of mouse pointer movements during our test. Table 1 shows the details of the
mouse pointer movement analysis.

Figure 11: Correlations of ESPiM and mouse pointer movements for the 9-to-5 group (Pearson’s $r=.71$, p $<$ .001). Figure 12: Correlations of ESPiM and mouse pointer movements for the flexible group (Pearson’s $r=.83$, p $<$ .001). Table 1: Descriptive statistics of mouse pointer movements. | Mouse Pointer Movements | Subjective Rating
---|---|---
| 9-to-5 | Flexible | 9-to-5 | Flexible
Mean | 667.14 | 565.11 | 1.34 | 2.00
Median | 617.00 | 454.00 | 1.00 | 2.00
SD | 283.22 | 266.50 | 0.59 | 0.93
IQR | 291.00 | 295.50 | 1.00 | 1.00
Range | 1121.00 | 1021.00 | 2.00 | 4.00
Min | 322.00 | 315.00 | 1.00 | 1.00
Max | 1443.00 | 1336.00 | 3.00 | 5.00

We also explored the effect of eye fixations on the calculated ESPiM
(t(34)=1.46, p $>$ .05) and found no significant difference between 9-to-5
($M=904.28~{}fixations$, $SE=64.06~{}fixations$) and flexible
($M=791.08~{}fixations$, $SE=76.35~{}fixations$) groups as illustrated in
Figure 10. This shows both groups of users experienced similar amount of eye
fixations during our test. Further, the Figure 10 shows the recorded errors
for both 9-to-5 and flexible groups.

Furthermore, we recorded participants’ symptoms of eye-strain after each
session as illustrated in Figure 13. It should be noted that these symptoms
are based on the subjective feedback from the participants and do not reflect
clinical definitions. We found that the flexible group reported more eye-
strain symptoms (29 symptoms) than the 9-to-5 group (15 symptoms).

The large gap between groups is related to tired eyes, dry eyes, and blurred
vision which can be explained due to the higher working hours on a computer
display of the flexible group as shown earlier in Figure 10. In simple words,
working in a ‘flexible’ routine seems to lead to more hours spent on a
display, which in turn leads to more eye-strain symptoms.

Figure 13: The recorded eye-strain symptoms of participants after the test.

Since participants ran the test on their personal computers at home, we
recorded different screen resolutions and analyzed the effectiveness of ESPiM
on different screens as illustrated in Figure 14. We found no significant
difference between the 9-to-5 group ($M=32.26~{}bits$,$SE=2.81~{}bits$) and
the flexible group ($M=30.83~{}bits$,$SE=3.25~{}bits$) based on screen
resolution, although there is a slight decrease of ESPiM for screen dimension
$1280~{}\times~{}720$ pixels for the 9-to-5 group.

Figure 14: The recorded ESPiM values of both groups based on screen
resolutions. The comparison points are shown as $P1$ to $P7$ which build
comparison sections to study ESPiM on screen expansion. We analysed the
increase and decrease of ESPiM in each section.

Additionally, we calculated the difference of ESPiM values for each resolution
point (shown as P1-P7 on Figure 14) to analyze the increase and decrease of
ESPiM values per resolution increase. In fact, we calculated $P_{n}-P_{n-1}$
($1\leq n\land P_{0}=0$) for each resolution as shown in Table 2. Negative
values represent decrease, and positive values increase of ESPiM per bits. The
summation of both groups shows a slightly higher increase for the flexible
group with the value of $32.11~{}bits$ compared to that of the 9-to-5 group
with the value of $32.01~{}bits$.

However, the difference lies only on a single resolution
($1280~{}\times~{}720$ pixels), this might suggest that users with a flexible
working time may experience higher eye-strain levels as screen dimensions
increase than the group of standard 9-to-5 routine.

However, the increase of eye-strain for both groups decreases for screen
dimensions larger than $2048~{}\times~{}1152$ pixels. This may suggest that
the choice of screen size is essential in how users experience eye-strain for
any working time schedules. This result might be of interest for video game
designers in finding suitable screen resolutions for their audience to
recommend for the best game experience with lower eye-strain levels.

Table 2: ESPiM difference based on screen resolution for both 9-to-5, and flexible groups. Positive values represent increase and negative values decrease of ESPiM per bits respectively.  Resolution (Pixels) | Measure | ESPiM 9-to-5 | ESPiM Flexible
---|---|---|---
1280 $\times$ 720 | P1 | 33.38 | 20.98
1280 $\times$ 800 | P2 - P1 | -11.28 | 1.10
1368 $\times$ 912 | P3 - P2 | 1.11 | 2.73
1920 $\times$ 1080 | P4 - P3 | 11.13 | 8.58
2048 $\times$ 1152 | P5 - P4 | 8.25 | 9.86
2560 $\times$ 1440 | P6 - P5 | -4.41 | -4.03
3440 $\times$ 1440 | P7 - P6 | -6.17 | -7.11
| $\Sigma$ | 32.01 | 32.11

This analysis is important as ESPiM takes both target’s and screen’s area into
account to calculate eye-strain value for comparison as shown in Equation 1.
Therefore, ESPiM is applicable on any screen resolution with no further
adjustments in its equation. This feature enables designers of user
interfaces, or producers of digital displays to compare different screen
dimensions based on eye-strain on consumers.

We also analyzed the error rates per screen resolution for both 9-to-5, and
flexible groups as shown in Figure 15. We recorded 11 errors for the 9-to-5
group compared to 3 errors for the flexible group. This suggests a higher
number of errors on smaller and larger screen dimensions for the 9-to-5 group.

Figure 15: The recorded errors of both groups based on screen resolutions.

We applied the K-means clustering algorithm ($K=4$) of the Scikit-learn
library in Python [36] on the center of target locations on screen recorded
for participants according to 4 equally-sized regions of the screen to analyze
the relative distance of (a) fixation points, (b) mouse click coordinates, and
(c) error locations recorded during the test. Since the mentioned measures
were distributed throughout the entire screen (see Figure 9 for eye fixation
points) on different screen dimensions, we applied the clustering algorithm to
downscale the locations into 4 points for comparison between measures of both
9-to-5, and flexible groups.

The results are illustrated in Figure 16, 17. This analysis was applied of the
center of targets illustrated as $C1$ to $C4$ on the plots to compare the
proximity of each measure based on working times (9-to-5 vs. flexible).
Interestingly, the locations of errors are almost similar for both groups.
Although the flexible group made a lower error rate (3 errors) than the 9-to-5
group (11 errors), the clustering results show the same regions for both
groups of participants.

The upper left region shows a shorter distance for both fixations, and mouse
clicks for both groups of participants labelled with $C1$, and $C2$ whereas
the lower regions (left and right) show a higher drift from the target center
points for mouse clicks illustrated as $C3$ and $C4$ on Figure 16, 17. In
addition, the lower regions of the 9-to-5 group show a short distance to
target centers for eye fixations (Figure 16), whereas the left lower region
shows a larger drift from the target center $C3$ for the flexible group as
shown in Figure 17.

Figure 16: The clusters of target center points ($C1$ to $C4$) with the
K-means algorithm ($k=4$) for mouse clicks, fixation points and errors of the
9-to-5 group. The dashed lines represent imaginary division borders of screen
into equal regions for comparison. Figure 17: The clusters of target center
points ($C1$ to $C4$) with the K-means algorithm ($k=4$) for mouse clicks,
fixation points and errors of the flexible group. The dashed lines represent
imaginary division borders of screen into equal regions for comparison.

Furthermore, we calculated the Euclidean distance to target centers for (a)
fixation points, and (b) mouse click coordinates for both groups to compare
the distance drift of each group from the center of targets as illustrated in
Figure 18. This suggests, that the flexible group recorded a higher distance
to target centers than the 9-to-5 group, however, the difference is
statistically not significant ($p>.05$), which may lead to a lower selection
accuracy based on center points.

Figure 18: The recorded Euclidean distance to target centers ($p>.05$) for (a)
fixation points, and (b) mouse clicks.

We demonstrated the application of ESPiM in a remote user study with a video-
based eye-tracking technique. We have shown the effectiveness of ESPiM for
user studies to analyze eye-strain on digital displays. Although the results
of ESPiM cannot be interpreted as clinical analysis, they can be applied in
low-budged user studies with physical restrictions to participants.

##  6 Limitations and Potentials of ESPiM

Although the ESPiM model is designed to be as flexible as possible, a precise
measurement of test execution time is required for any user study. Thus an
accurate synchronization between the start and end of a test is needed.
Further, it does not take preparation activity times into account, for
instance, spent time on the calibration process before running a test.
Moreover, the ESPiM model represents a theoretical model for eye-strain,
however, this model does not represent medical/clinical measures and therefore
is not applicable in these fields of study.

Beyond the applications of ESPiM mentioned before in section 3.1, this model
can also be suitable for analyzing Microsleep, a sudden loss of awareness or a
short sleep in car drivers. Especially for truck drivers, this could reduce
traffic collisions by measuring eye-strain levels for different times and
conditions in user studies. Researchers may apply machine learning techniques
to run or expand the model in real-time use case scenarios to detect driver’s
awareness based on eye-strain through integrated eye-tracking systems into car
dashboards. In addition, the ESPiM model can be applied in research studies
involving wearable eye-tracking devices, and biological sensors to study the
impacts of visual distractions among knowledge workers.

##  7 Conclusion and Future Work

Eye-strain is a common issue among computer users due to prolonged periods
spent working and using digital displays, which leads to vision problems such
as irritation and tiredness of the eyes and headaches. We proposed the Eye-
Strain Probation Model (ESPiM), an easy-to-apply computational model to
measure eye-strain on digital displays based on the spatial properties of the
user interface and display area for a required period of time, using eye-
tracking analysis integrated into a single measure.

We conducted two user studies to evaluate the effectiveness of ESPiM, its
functionalities and potentials and showed how to measure potential eye-strain
levels of a specific user interface suitable for pilot studies to compare
various design prototypes before the application by end-users.

We evaluated the effectiveness of ESPiM in an in-person user study with an
infrared eye-tracking sensor and found interesting patterns among video
gameplay play frequency groups. In addition, we showed the application of
ESPiM in a remote user study that complied with the COVID-19 safety measures.
Since eye fixations can be assessed, which is typically bound to the range of
(200-600 ms), ESPiM can be applied in pilot studies without access to eye-
tracking devices for analysis estimations.

Despite the relatively short test session for each participant of both user
studies, we evaluated our hypotheses and identified distinctive patterns among
participants. Furthermore, we showed that ESPiM has strong correlations with
the error rates of target selections. The correlation with error rates can be
interpreted as an indicator to estimate and reduce the impacts of the Midas
touch problem in gaze-based interactions by analyzing the user interface
properties.

We also found significantly different eye-strain patterns based on the video
gameplay frequency of participants. The results showed that participants with
frequent video gameplay reached significantly lower eye-strain and lower error
rates than their counterparts. This may suggest that users with higher
training of eye focus shifts (e.g. video games) might experience a lower eye-
strain with prolonged use of digital displays for singular (not multi-tasking)
or enjoyable tasks. Furthermore, we found that mouse pointer movements
increase as eye-strain levels increase. This would suggest users tend to move
their mouse pointers more frequently to select targets in case of tired eyes.
Beyond the prediction and measurement of eye-strain, ESPiM can be applied to
compare different gaze-based interaction techniques and evaluate different
user interface prototypes to reach a comfortable design based on eye-strain.

As more individuals get access to digital content provided on digital
displays, especially children and students of a younger age, digital media
consumption becomes more prevalent. Furthermore, since the start of the
COVID-19 pandemic, many schools have moved to online teaching, which has
caused new challenges for elementary students and their parents. Many in-
person events still occur on digital devices now to comply with the required
safety measures. Thus, there is a need for compound models to cope with the
emerging trends in order to measure and compare the impact of digital displays
on our health. Today, most smartphones include high-quality cameras accessible
by younger generations. Thus the application of video-based eye-tracking that
we showed in this paper can be feasible to design and conduct large-scale user
studies on smartphones to reach a larger population of digital media
consumers.

In future work, we plan to apply ESPiM on smart-phones to compare educational
video games for school students. Additionally, the continuous and predictable
form of ESPiM makes it suitable for machine learning algorithms which will be
explored in the future. Finally, we hope our proposed model makes a step
forward towards reducing eye-strain on digital displays, inspires researchers
and user interface designers and leads to more discussions in research
communities.

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