Next Article in Journal
Behind the Scenes: Impact of Virtual Backgrounds in Educational Videos on Visual Processing and Learning Outcomes
Previous Article in Journal
An Eye Tracking Based Framework for Safety Improvement of Offshore Operations
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JEMR
Volume 16
Issue 3
10.16910/jemr.16.3.3
jemr-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
Journal of Eye Movement Research is published by MDPI from Volume 18 Issue 1 (2025). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Bern Open Publishing (BOP).
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Eye Tracking in Optometry: A Systematic Review

by
Leonela González-Vides
1,2,*,
José Luis Hernández-Verdejo
1 and
Pilar Cañadas-Suárez
1
1
Complutense University of Madrid, Madrid, Spain
2
University of Costa Rica, San Pedro, Costa Ric
*
Author to whom correspondence should be addressed.
J. Eye Mov. Res. 2023, 16(3), 1-55; https://doi.org/10.16910/jemr.16.3.3
Submission received: 11 September 2022 / Published: 16 August 2023
Browse Figures
Versions Notes

Abstract

:
This systematic review examines the use of eye-tracking devices in optometry, describing their main characteristics, areas of application and metrics used. Using the PRISMA method, a systematic search was performed of three databases. The search strategy identified 141 reports relevant to this topic, indicating the exponential growth over the past ten years of the use of eye trackers in optometry. Eye-tracking technology was applied in at least 12 areas of the field of optometry and rehabilitation, the main ones being optometric device technology, and the assessment, treatment, and analysis of ocular disorders. The main devices reported on were infrared light-based and had an image capture frequency of 60 Hz to 2000 Hz. The main metrics mentioned were fixations, saccadic movements, smooth pursuit, microsaccades, and pupil variables. Study quality was sometimes limited in that incomplete information was provided regarding the devices used, the study design, the methods used, participants’ visual function and statistical treatment of data. While there is still a need for more research in this area, eye-tracking devices should be more actively incorporated as a useful tool with both clinical and research applications. This review highlights the robustness this technology offers to obtain objective information about a person’s vision in terms of optometry and visual function, with implications for improving visual health services and our understanding of the vision process.

Introduction

Eye-tracking technology was first introduced in the 19th century and was mainly used to analyze reading processes in people. Other applications described have been its use in clinical neuropsychology and in controlling computers through gaze (Holmqvist & Anderson, 2017; Pluzyczka, 2018).
Recent advances in eye-tracking technology have had an impact in the field of health, including optometry. Today’s commercial devices have little to do with their predecessors, and the present is known as the third era of eye trackers with a greater capacity to record and process data (Holmqvist & Anderson, 2017).
Eye trackers consist of a system with a sensor to detect, measure, and capture eye movements and eye positions. Individual eye movements and what the individual is looking at are tracked through different mechanisms such as using an artificial infrared light source that generates a reflection on the cornea, tracking pupil position, and appearance-based eye tracking. Other hardware systems, such as webcams and smartphones, have also been used (Punde et al., 2017; TobiiPro, 2015; Valliappan et al., 2020). These devices also work with special software to process the data and interpret the information obtained.
A multitude of techniques exists to record eye movements, including mirror reflection systems, electrooculogram systems, photoelectric and video-based limbus tracking, sclera coils, canthus and corneal bulge tracking, tracking retinal features, dual Purkinje imaging, dark and bright pupil tracking, pupil and corneal reflection, laser-based pupil and iris tracking, video-based tracking of artificial markers, and the most common method, pupil center corneal reflection (Holmqvist & Anderson, 2017). These techniques are employed by devices such as screenbased eye trackers or wearable eye trackers to collect information.
Eye tracking devices enable the monitoring and recording of eye movements, providing valuable information that can be extracted as raw data samples, including pupil size, pupil position, corneal reflections, fixation and saccade velocities and accelerations, gaze vectors for each eye, and gaze points. Other metrics, such as fixations, saccades, post-saccadic oscillation, smooth pursuit, microsaccades, tremor, and drifts, can be derived from these gaze-related event metrics (Duchowski, 2007; Engbert & Kliegl, 2003; Holmqvist & Anderson, 2017; König & Buffalo, 2014; Punde et al., 2017; Salvucci & Goldberg, 2000; Schweitzer & Rolfs, 2020; Zemblys et al., 2019).
Eye-tracking metrics can facilitate the acquisition of relevant information regarding various aspects of human behavior. Accordingly, eye trackers are used in cognitive psychology, to analyze human-computer interactions, and in marketing, psycholinguistics, neurolinguistics, and sports science, among others (Holmqvist & Anderson, 2017; Romano & Schall, 2014). The basis for these applications is that eye movements provide different levels of information, including gaze properties, eye properties, perception properties, characteristics of cognitive processes, and even opinions and ideas about people’s reasoning and clinical aspects of different pathologies (Adams et al., 2017; Al-Haddad et al., 2019; Economides et al., 2021).
The data obtained from these systems can be used for vision assessment, as they offer precise information and metrics on ocular behavior. Eye-tracking technology is also employed in vision analysis instruments that have frequent applications in ophthalmology. Examples are eye trackers associated with optical coherence tomography (OCT), scanning laser ophthalmoscopy and microperimetry.
While eye trackers provide excellent data on certain eye movements, they are highly sophisticated and can be cumbersome to transport (Rahn & Kozak, 2021) limiting the possibilities of modifying the stimuli received or analysis conducted in real-world contexts. In addition, as occurs with the microperimeter, they only allow for monocular tests. As in most individuals the eyes move congruently, a monocular analysis may not offer a comprehensive understanding of human behavior in reallife situations, such as under conditions of low vision.
To circumvent this limitation, new eye tracker devices have been developed for clinical optometry applications. Among these instruments, we should mention screenbased devices and special glasses designed to facilitate manipulation of the stimulus received, the adaptation of tasks, and the selection of eye movements to analyze. These characteristics of eye trackers expand their possibilities to include adjusting instrument calibration or the presentation of stimuli to analyze visual function in specific populations such as children or persons with low vision.
The use of this technology also requires being aware of factors enabling the collection of high-quality data. These include the device’s characteristics, sampling rate, accuracy average, the eye-tracking mechanism, and eyetracking setups or methods of testing. In addition, the importance of calibration, control of head movements, and the characteristics of the environment, such as lighting, temperature and noise, must be considered.
This is why it is important to examine how, up until now, this technology has been used in optometry. Moreover, given that eye trackers have reached a new level of technological readiness, it needs to be established whether these devices and their optometric applications are ready for use in clinical practice.
So far, several review studies have examined the evolution of eye-tracking technology. In 2017, one such review examined the use of microperimetry to assess visual function in age-related macular degeneration. The authors of this study highlighted the benefits of incorporating an eye-tracker in a microperimetry system to correct the position of the stimulus for changes in fixation (Cassels et al., 2018).
In 2020, another study addressed the use of eye tracking in ophthalmology, indicating that this technology is used in modern imaging instruments for patient assessment and imaging diagnostics. This technology also seems to have applications in ophthalmic and refractive surgery (Rahn & Kozak, 2021). However, literature reports of optometry research in general have not discussed the use of this technology in depth, although it has been much used in clinical practice.

Methods

Design

This systematic review was designed to assess the benefits of eye tracking in the field of optometry. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to describe the information collected and registered our study protocol with PROSPERO (registration number 364762). The flow of the PRISMA search is detailed in Figure 1. RevMan software (version 5.4, Cochrane, UK) was used to record information.

Search Strategy

The search was conducted in January 2022 and rechecked in July 2022. Titles, abstracts, and bodies of texts were searched in the databases Scopus, Web of Science, and PubMed using the descriptors: ((eye track**) AND (visual acuity)) AND (eye movements) AND (assessment) NOT ((drugs) NOT (psychology). Visual acuity was included as visual function is usually related to visual acuity. This descriptor also helped restrict our search to studies relevant to optometry.

Study Selection

The following inclusion and exclusion criteria were consistently applied throughout the process: 1) papers published between 2017 and 2022 were included to obtain the most recent information; 2) titles and abstracts related to the field of vision; 3) studies conducted in humans; 4) studies using eye-tracking devices as part of their methodology; 5) papers published in English; and 5) conference papers and journal articles. The review excluded editorials, reviews, studies on animals and reports on cognitive function or psychology-related topics.
Inclusion criteria were applied and each title and abstract was screened by two independent reviewers. When a title or abstract provided insufficient information, the reviewers discussed and made decisions to resolve any disagreement. This served to avoid the risk of missing important information and potentially eligible articles.

Data Extraction and Quality Assessment

The reviewers independently reviewed full texts and extracted important data in a collection form. The data extracted for each study were year of publication, main objective, subjects, device characteristics, and metrics/paradigms used. As the review sought to identify the areas of optometry in which eye-tracking systems are used, the parameters extracted could vary from one study to another.
Study quality was assessed based on the following criteria: number of participants, method implemented, reporting of the characteristics of the eye tracker used, and the way in which it was employed.

Statistical Analysis

Given the nature of this systematic review and the lack of quantitative data provided by many of the studies included, the results of our review are presented in narrative form.

Results

In the search and selection process, 1340 reports were identified, of which 714 remained after removing duplicates. Next, the selected articles were screened according to their publication date, language, title, and abstract, leaving 181 candidate reports. Six papers were excluded because they used animals in the studies. The remaining 175 full-text articles were assessed for eligibility, leaving only 141 reports after discarding 34 as they did not exactly relate to the topic.
The initial database search yielded 1340 reports on the use of eye trackers in the field of vision since 1968 (Figure 2). In the past ten years, studies related to the use of eye tracking devices in optometry have undergone an exponential increase, averaging at around 17 articles per year. The year 2018 stands out from the rest as there were 24 scientific publications on the topic.

Eye Trackers in the Field of Optometry

After an exhaustive review of all 141 selected reports, these were grouped by area of application in the field of optometry as described below. In Table 2, the information is organized by area of application, main metrics, devices used, benefits of eye tracking, and methodological aspects.

Nystagmus

Nystagmus is an involuntary oscillatory movement of the eyes that can affect a person’s visual acuity (Rosengren et al., 2020a). This disorder was analyzed by eye tracking in four of the articles reviewed here. Different types of nystagmus were examined such as voluntary flutter during ophthalmoscopy (Norouzifard et al., 2020; Thomas et al., 2022) or optokinetic nystagmus (Norouzifard et al., 2020). Some authors highlighted the capacity of eye-tracking devices to adequately assess individuals with this condition (Rosengren et al., 2020a).

Visual Acuity

Six of the articles reviewed described studies in which visual acuity was examined as the ability to see details using eye-tracking devices. Four of the studies conducted an analysis of dynamic visual acuity (Ağaoğlu et al., 2018; Chen & Yeh, 2019; Domdei et al., 2021; Palidis et al., 2017) while two investigations focused on behavior, visual control, and detection of targets in situations of reduced visual acuity (Freedman et al., 2018, 2019).

Visual Field

Eye-tracking devices are also used in visual field exams. Nine publications were found on this topic describing studies in which eye movements were used to grade visual health (Mooney et al., 2021) and classify visual field loss (Grillini et al., 2018; Murray et al., 2018). The authors of these studies also explored how vision and eye movements function in a monocular or binocular way when there is central visual field involvement. In individuals with hemianopia, the use of these devices to determine the preferential retinal locus was described in subjects with a macular disorder (Barraza-Bernal et al., 2017a; Barraza-Bernal et al., 2017b; Murray et al., 2018; Shanidze et al., 2017; Woutersen et al., 2020).

Amblyopia/Strabismus/Vergences

Seven of the articles reviewed addressed amblyopia, strabismus and vergences. In one study, anomalies in monocular and binocular motility were analyzed in children with amblyopia (Murray et al., 2022). Eyetracker devices were also used as a resource to detect strabismus, mainly characterizing skills for fixation stability (Al-Haddad et al., 2019; Kelly et al., 2019a; Zrinscak et al., 2021) and visual searching (Aizenman & Levi, 2021; Satgunam et al., 2021; Tsirlin et al., 2018).
Three of the studies reviewed focused on the topic of exotropia as one of the most common types of strabismus. In these studies, the factors considered were exophoria, intermittent exotropia (Adams et al., 2017; Economides et al., 2021), and longitudinal changes in slow visual searches in subjects with this type of exotropia undergoing surgery (Mihara et al., 2020). Ocular incompatibility and dominance were also addressed by some authors (Adams et al., 2017).
Nine of the studies identified the use of eye-tracking devices to analyze visual functions. These included characteristics such as convergence, divergence, accommodation, and stereopsis. Eye movement data were collected when taking measurements at the near-point of convergence or vergence (Feil et al., 2017; Kim et al., 2022; Namaeh et al., 2020; Ramakrishnan & Stevenson, 2020). Some studies even used technology to perform automated measurements of phorias and heterophorias and determine the influence of age on adaptation to disparity (Alvarez et al., 2017; Gantz & Caspi, 2020; Mestre et al., 2017, 2018, 2021).

Surgery

Three articles examined ocular motility in relation to surgery: one study after strabismus surgery (Perrin-Fievez et al., 2018), another assessed the use of eye trackers in small-incision lenticule extraction (SMILE) procedures (Reinstein et al., 2018), and finally, Coletta and colleagues examined the impact on eye motility of refractive surgery (Coletta et al., 2018).

Technology/Visual Equipment/Virtual and Augmented Reality

Some technological devices, such as video games and virtual and augmented reality systems, incorporate gazetracking systems that enable the capture of gaze data. Our review found 18 articles addressing topics related to hardware or software used to improve techniques such as calibration methods, foveated rendering, or the use of portable devices (Albert et al., 2017; Cheong et al., 2018; Esfahlani et al., 2019; Fujimoto et al., 2022; Jones et al., 2019; Kim et al., 2019; Love et al., 2021; Pundlik et al., 2019; Rosengren et al., 2020b). These studies highlight a need for more robust eye trackers for use in different instruments including OCT and microperimetry devices (Chopra et al., 2017; Essig et al., 2021; Hirasawa et al., 2018; Hirota et al., 2021; Lauermann et al., 2017; Mao et al., 2021; Murray et al., 2017).

Ocular Pathology/Low Vision

Nineteen publications documenting the study of ocular disorders were found. The main diseases examined with eye-tracking technology were glaucoma, Stardgart’s disease, age-related macular degeneration, diabetic macular edema, and sequellae of concussion. These investigations characterized and examined in detail oculomotor aspects, fixation stability and binocular fixation (Abadia et al., 2017; Asfaw et al., 2018; Ballae Ganeshrao et al., 2021; Barsingerhorn et al., 2019; Gao & Sabel, 2017; Garric et al., 2021; Giacomelli et al., 2020; Jakobsen et al., 2017; Kooiker et al., 2019; Laude et al., 2018; Lee et al., 2017; Senger et al., 2020; Shivdasani et al., 2017; Tarita-Nistor et al., 2017; Titchener et al., 2020). Aspects related to visuospatial orientation and visual comfort were also analyzed (Ballae Ganeshrao et al., 2021; Garric et al., 2021; Kooiker et al., 2019; Leonard et al., 2021). One study was designed to explore how binocular eye movements behave in the presence of scotoma in terms of binocular vision and contrast sensitivity in peripheral vision (Alberti & Bex, 2017). Other authors used an eye tracker to analyze the eye movements of drivers diagnosed with glaucoma (Lee et al., 2019).

Assessment/Diagnosis/Rehabilitation/Training

Most eye-tracking research effort was found centering on the assessment, diagnosis, and treatment of various ocular conditions. We therefore selected 24 articles describing the use of ocular motility parameters for such purposes. In these studies, eye-tracking devices were employed to assess color vision (Taore et al., 2022), contrast sensitivity (Tatiyosyan et al., 2020), and visual function (Wilhelmsen et al., 2021). Ocular motility was used as a biomarker of visual function beyond visual acuity (Brodsky & Good, 2021; Liston et al., 2017; Liu et al., 2017), to develop simple tests to assess slow-to-see behavior in children (Weaterton et al., 2020), to evaluate attention in children with high-visual acuity (Ramesh et al., 2020), and to analyze visual comfort and visual acuity changes in terms of microsaccades (Shelchkova et al., 2019). Eye tracking has also been used to assess nonpathological aspects of eye disorders such as visual fatigue (Mooney et al., 2018; Ryu & Wallraven, 2018; Schönbach et al., 2017; Tatiyosyan et al., 2020; Wang et al., 2020; Wen et al., 2018; Xie et al., 2021), and to identify the preferred retinal locus when there is scotoma in persons with macular degeneration (Yow et al., 2017, 2018). These publications also report on the use of eye trackers to restore visual capacity, and to train visual fields and visual searching(Awada et al., 2022; Axelsson et al., 2019; Chatard et al., 2019; Hotta et al., 2019; Mooney et al., 2021; Perperidis et al., 2021; Tatiyosyan et al., 2020; Wan et al., 2020).

Refractive Error

Two of the studies reviewed examined ocular motility according to refractive error (Doustkouhi et al., 2020; Ohlendorf et al., 2022).

Reading in Optometry Assessment

Nine of the reports reviewed analyzed patterns of ocular motility during reading in both healthy subjects and in those with conditions such as nystagmus (Fadzil et al., 2019) or delayed reading (Vinuela-Navarro et al., 2017). These studies were conducted mainly in children or individuals with visual field loss (Ridder et al., 2017). Stimuli used were short, long, and dynamic texts, and blue light filters (Ryu & Wallraven, 2017). Some authors also compared rapid serial visual presentation and horizontal scrolling text (Bowman et al., 2021; Hyona et al., 2020; Murata et al., 2017; Perrin-Fievez et al., 2018). In other studies, only the benefits of the use of eye trackers during reading were explored (Wertli et al., 2020).

Sports Vision/Locomotion

Another vision field that has been growing in recent years is sports vision, but only one article dealing with this topic was identified. The authors of this report analyzed changes produced in visuomotor behavior in children during training in combat sports (Ju et al., 2018).

Oculomotor Deficits/Oculomotor Responses

Two of the articles reviewed focused on oculomotor responses. One study addressed oculomotor behavior in response to changes at the vestibular level, and the other study was designed to compare oculomotor deficits in adopted and non-adopted children from an unspecified region of Europe (Pueyo et al., 2020; Wibble et al., 2020).

General Eye Movements

Finally, twenty-one of the articles included in our review examined ocular motility in general without focusing on any given population. While these reports cannot be assigned to any of the previous sections, their findings have contributed to the field of optometry. Some of the studies described were designed to determine how much time one needs to fixate during different tasks (Belyaev et al., 2020; Bowers et al., 2021; Chaudhary et al., 2020; Ivanchenko et al., 2019; Kelly et al., 2019b; Ratnam et al., 2018; Seemiller et al., 2018), whether fixation stability leads to reduced head movements in people with Argus II (Caspi et al., 2018), or whether this stability differs between central and peripheral vision (Raveendran et al., 2020).
These studies also analyzed metrics related to different aspects of saccades, such as saccadic rhythmicity (Amit et al., 2017; Poletti et al., 2020; Sheynikhovich et al., 2018), presaccadic motion (Kwon et al., 2019), patterns of saccades (Badler et al., 2019), characteristics of small saccades and microsaccades (Fang et al., 2018; Nanjappa & McPeek, 2021), dynamic perisaccades (Intoy et al., 2021), smooth pursuit (Goettker et al., 2019; González et al., 2019), and visual perception (Pel et al., 2019; Vater et al., 2017). Some articles highlighted data on disparity and deviations between the eyes and different positions of gaze (Barraza-Bernal, Rifai et al., 2017a, 2017b; P. Liu et al., 2021; Namaeh et al., 2020; Ramakrishnan & Stevenson, 2020; Wibble et al., 2020).

Methods Used

Main Devices and Their Characteristics.

Eye tracking devices use different methods of collecting information on eye positions. In most of the studies reviewed, this was pupillary corneal reflection. In Appendix A, the devices used by area of application are listed. Four eye tracker brands emerged as most used in the field of optometry: Eyelink 1000 (SR Research, Ontario, Canada) (n=37), those of the company Tobii (TobiiTechnology, Stockholm, Sweden) (n=36), Dual Purkinje eye tracker (Fourward Technologies) (n=5), and the SMI eye tracker (SensoMotoric Instruments GmbH, Teltow, 95 Germany) (n=7).
Some of the eye trackers are incorporated in other technological systems used for vision assessment such as microperimeters (n=2) and tracking scanning laser ophthalmoscopes (n=3). Across all references, these devices have an imaging capture frequency between 60 Hz and 2000 Hz, and are based on the infrared light technique. Both head-mounted and screen-based devices have been employed, depending on whether the exam setting is realworld or controlled environment, respectively.

Stimuli Setups and Recordings

Owing to the diversity of study populations and objectives, stimulus setups varied widely in terms of color, size, and shape. Setups ranged from a simple black fixation point in primary gaze position or in different positions on the screen, to smooth pursuit stimuli and cartoon videos for pediatric populations (Cheong et al., 2018; Pueyo et al., 2020; Vinuela-Navarro et al., 2017), images of real-life situations (Albert et al., 2017), or standardized tests assessing reading or ocular motility (Ridder et al., 2017; Taore et al., 2022; Woutersen et al., 2020). Some stationary and dynamic stimuli were created by the researchers mainly using programs like Matlab and Python.
Each experiment started with some calibration procedure. These ranged from using two dots in two different positions, to as many as nine dots. Only a few research groups reported on the accuracy considered acceptable for calibration (1º of accuracy) (Adams et al., 2017; Economides et al., 2021).
Additionally, the distance between the individual and stimulus varied from 20 cm to 6 m, the average distance in most experiments being 55 cm. For each experiment or trial, durations varied as they depended on the individual’s reaction.

Main Metrics Used and Statistical Analysis

According to the data obtained, the main metrics used in the studies as a source of information were those related to fixations (n=60), saccadic movements (n=41), eye position (n=25), smooth pursuit (n=13), and microsaccades (n=10). For each motility measure, the factors analyzed were number and total average, total time, average time, frequency, amplitude of motion, total time in activity, and reaction time. On occasion, data were analyzed both monocularly and binocularly.
Event-related measures were mainly used to locate any important event (metrics) while recording the timeline. In this process, eye movements were first recorded and then metrics were analyzed. In a few reports, algorithms were implemented to obtain quantitative data. The most used procedure was the bivariate contour ellipse area (BCEA) method, which served to calculate fixation stability.
Only some reports indicated the type of research conducted: prospective experimental cohort study (Garric et al., 2021), comparative study (Tatham et al., 2020), prospective study (Wan et al., 2020), longitudinal study (Kooiker et al., 2019), cross-sectional studies (Hirasawa et al., 2018; Jakobsen et al., 2017; Laude et al., 2018; Murata et al., 2017; Murray et al., 2022), and case-control study (Gao & Sabel, 2017). Twenty-seven of the articles reported having established a control group with which to compare data from subjects with eye disease.
In general, the articles mentioned several modes of statistical data treatment. Most reports provided descriptive statistics (mean, median, standard deviation, interquartile range), and described the use of comparison and correlation tests on their datasets, and dependent and non-dependent groups for parametric and nonparametric data (Appendix A).

Discussion

According to our database search, eye-tracking devices have been used for scientific research in the field of optometry since 1968. The year 2018 stands out as the year in which there was the largest number of publications related to this topic. Within the field of optometry, the use of these devices has been described in at least 12 different areas.
Our comparison of the different studies identified and reviewed was hindered by the diversity of data handled, mainly because of the different populations, eye-tracking devices, and metrics involved.

Main Areas of Application

Among the main application areas of eye trackers (optometry device technology, and assessment, treatment, and analysis of eye conditions), our review revealed the diagnosis and treatment of different eye conditions as a constant interest in optometry. Consistently, in recent years, eye-tracking technology has been used to improve vision assessment, expand vision care services, for example, to include applications of eye trackers in telemedicine, and even allow for the early detection or and/or prevention of certain eye conditions (di Stefano, 2002). For these purposes, eye-tracking programs are useful tools as they can be easily integrated into commonly used technologies such as tablets, cell phones, and computers (Jones, 2020; Jones et al., 2019).

Devices Used and Their Characteristics

The most common characteristic of the eye-tracking devices employed in the studies reviewed were infrared light technology, involving a capture frequency greater than 60 Hz, although most exceeded 125 Hz (Holmqvist & Anderson, 2017). The characteristic features of the four most commonly used devices are compared in Table 2. Devices marketed by Tobii and Eyelink seem to be the preferred systems yet their availability in some institutions is somewhat limited by their high cost.
The mechanism of action of most devices reported on was the corneal reflex technique. However, some research groups used eye trackers based on pupil-corneal reflection to obtain more static measurements, such as fixations, as this technique is considered ineffective to study the dynamics of eye movements or dynamic saccadic movements (Hooge et al., 2016; SR-Research Eye Link).
Most eye trackers were screen-based devices. These allow for a more controlled environment, the presentation of specific stimuli on screen, and even the incorporation of a chinrest, which is recommended to control head movements (Ju et al., 2018; Thomas et al., 2022).
Some studies reported on the use of head-mounted trackers. Such devices are useful for taking measurements in different environments and even during persons’ daily activities or real-life situations (Abadi et al., 2021; Chaudhary et al., 2020; Chow-Wing-Bom et al., 2020; Freedman et al., 2019; Fujimoto et al., 2022).
It should be mentioned that almost all the reviewed studies used experimental setups such that the devices used were not designed as optometric diagnostic systems. This meant that extensive individual analysis of each person may not have been possible. Currently, only especially manufactured optometric devices have wide applications in clinics.

Main Metrics Used

Many of the studies reviewed were designed to analyze fixations, saccadic movements, smooth pursuit, microsaccades, and pupil variables, as the typical ocular movements used to generate metrics (Holmqvist & Anderson, 2017). Most reported data were obtained by segmenting the experiment into distinct tasks or events. This allowed for an improved analysis of ocular behavior at specific moments and made it possible to assess the total number of fixations or saccades produced and their durations and amplitudes, visual search patterns, smooth pursuit, and reaction time.

Methods and Statistical Aspects

After analyzing certain methodological aspects, we found that only 10 articles provided information on the type of study conducted. Most studies were cross-sectional and designed to examine the prevalence and diagnosis of patient conditions such as nystagmus, low vision, and strabismus, among others. In most reports, the sample size estimation method used was unclear, and an arbitrary size was mentioned (Chaudhary et al., 2020). Only one group reported on the use of Cochran’s formula (Fadzil et al., 2019).
Subject population ages varied from children to adults, revealing the wide applicability of eye trackers. As these are automatic capture devices for which participation or active response of the subject is not always required, they can be employed to assess behavior and visual function in newborn children (Kelly et al., 2019; Perperidis et al., 2021) or older adults (Cheong et al., 2018; Chopra et al., 2017; Gonzalez et al., 2019; Lauermann et al., 2017; Lee et al., 2017, 2019; Liu et al., 2017; Shanidze et al., 2017; Sheynikhovich et al., 2018; Yow et al., 2018).
In some papers, the specific use of the metrics collected or even the eye-tracking paradigm applied were not specified. It is important to study a specific phenomenon and design the experiment properly (Holmqvist & Anderson, 2017). Only nine studies indicated in their methodology the eye-tracking paradigm addressed (Al-Haddad et al., 2019; Badler et al., 2019; Ballae Ganeshrao et al., 2021; Barsingerhorn et al., 2019; Kooiker et al., 2019; Pel et al., 2019; Ramesh et al., 2020; Vater et al., 2017; Wen et al., 2018), especially those assessing eye movements in general, in which the goal was to analyze ocular behavior.
Unlike other studies where eye trackers serve to learn about emotional or cognitive aspects, optometry studies may mainly involve the use raw eye movement data. Many of the studies reviewed, however, had limitations in reporting quality (quality information, statistical power, calibration accuracy, and data loss, among others). Moreover, some did not clearly specify design characteristics and environmental factors (such as device model, frequency of image capture, position, illumination, and temperature). Only one article mentioned having done a pilot study (Pel et al., 2019), as is usually recommended for eye-tracking studies.
Most articles centered on ocular pathologies and low vision reported a large volume of data on visual function. In addition, authors mentioned calibration changes made to ensure subjects could see the stimulus presented. This is important as in this specific area of optometry, extensive subject characterization in terms of visual acuity, contrast sensitivity, or visual field is needed because eye movements can be affected by these factors (Blignaut et al., 2019). Other factors that may need to be specified depending on the main study objectives are pupil size, the number and duration of fixations, saccadic movements and regressions, and eye video, as well as the type of stimulus used, its size and color, the conditions of the space and screen features (Thomson, 2017).
With regard to the statistical treatment of the data obtained, besides providing descriptive statistics such as means, medians, and standard deviations, non-parametric tests were mainly used (Mann-Whitney U, Wilcoxon test, Kruskal Wallis test, Friedman test) as most studies were based on non-representative samples. Three articles patently explained the limitations of eye trackers in their studies, such as calibration problems (Doustkouhi et al., 2020; Mooney et al., 2021), reflection from the surface of lenses (Doustkouhi et al., 2020), the use of glasses, and sample size (Wertli et al., 2020).
Further, although the quality of the evidence provided varied between articles, fourteen studies directly analyzed the usefulness of eye trackers and offered relevant data regarding the validity of their clinical application. Some authors also emphasized the effectiveness and potential of eye trackers, especially for the identification and rehabilitation of patients with ocular disorders, specifically those with visual field loss, through the use of gaze data (Laude et al., 2018; H. Liu et al., 2017; Ratnam et al., 2018; Yow et al., 2018).
Our review identified a need to continue promoting research in this area and to replicate studies with sufficient methodological weight to extrapolate results to the general population.

Technological Readiness

Technology Readiness Level (TRL) is a systematic method designed to grade the maturity level of a given technology. It consists of nine levels:
TRL 1: Basic principles observed
TRL 2: Technology concept formulated TRL 3: Experimental proof of concept
TRL 4: Technology validated in the laboratory TRL 5: Validated in a relevant environment
TRL 6: Technology demonstrated in a relevant environment
TRL 7: System prototype demonstration in an operational environment
TRL 8: System completed and qualified
TRL 9: System proven in operational environment (Mankins, 1995).
Many of the devices employed in the studies reviewed, such as those manufactured by Tobii® and Eyelink®, are already marketed and employed in different contexts, including real-life scenarios. However, according to a TRL analysis of the applications of eye-tracking technology in optometry, most studies fulfill the criteria of the first four levels. These levels are related to proof of concept, whereby research is conducted in a laboratory setting in a controlled environment to emphasize that this new technology is valid. Results are presented as measures of specific parameters or metrics.
While several studies have attempted to utilize eye movement measurements in real-world scenarios, such as face recognition in sports or driving (Ju et al., 2018; Lee et al., 2019; Weaterton et al., 2020), these efforts have only yielded basic metrics, rather than new software or hardware applying eye-tracking technology.
One such study reported on the use of a DIVE device (Pueyo et al., 2020), a product based on eye-tracking technology that is currently marketed for clinical optometry practice. This device is specifically designed to assess visual function and ocular motility in children. We suggest that in consequence this technology could be graded as TRL level 8 as it is relatively new to the market and not yet widely used.
Eye-tracking devices are still expensive, and their use also requires precision and basic knowledge to avoid handling difficulties leading to erroneous data. In consequence, their clinical applicability in optometry is relatively incipient, and the studies available can be described as pre-clinical. Notwithstanding, the results of some of the studies reviewed here reveal the potential of eye-tracking technology to develop preliminary eye movement models in patients (Fujimoto et al., 2022), as well as vision tests to investigate the impacts of visual training (Cheong et al., 2018). This suggests that this technology can be applied to various stages of clinical practice, including assessment, intervention, and treatment monitoring.
According to the results of our review, eye-tracking systems are set to have an impact on optometry practice, but it is crucial that we continue to work on new ideas to improve on the readiness of this technology, including the development of standardized, simplified validated procedures.
As limitations of our review, we should mention the unavailability of data emerging from optometry eyetracker studies. This is not the case in other fields such as psychology and neuroscience. This review, however, provides a comprehensive overview for optometry professionals wishing to investigate or apply eye-tracking systems in clinical practice. By providing information on the devices and metrics used, we hope to encourage new lines of research with practical implications for patient care.

Conclusions

The use of eye-tracking devices in optometry has exponentially grown over the past ten years, such that this technology is now used in at least 12 areas of optometry and rehabilitation, but mostly in the areas of technology, and the assessment, treatment, and analysis of ocular disorders.
Eye trackers use data from the visual system and ocular motility information to record the visual behavior of individuals of any age both in natural and controlled environments, thus expanding possible applications from laboratory settings to clinical practice.
We propose that these tools should be incorporated more actively in optometry, both in research and clinical applications, as they offer robust objective information about an individual’s vision in terms of optometry and visual function, with the ultimate goal of improving visual health services and our understanding of the vision process.
For the use of eye trackers in clinical practice, it is important that professionals have precise knowledge of the characteristics of the software and hardware used to ensure the acquisition of valid data while considering their limitations. It is especially important that new procedures become standardized, simplified in their application, and validated. It is clear that eye-tracking systems will gradually gain popularity in the optometry field, not only for assessment but also for treatment and training.

Ethics and Conflict of Interest

The authors declare that the contents of this article are in agreement with the ethics described in http://biblio.unibe.ch/portale/elibrary/BOP/jemr/ethics.html and that there is no conflict of interest regarding its publication.

Acknowledgments

The authors acknowledge the University of Costa Rica for financial support provided to Leonela González Vides in her academic training abroad as a fellow under the Academic Mobility program of the Office of International Affairs and External Cooperation.

Funding sources

No funding was received from funding agencies in the public, commercial, or non-profit sectors.

Appendix A. Main devices and metrics used in the different areas of optometry

Jemr 16 00015 i002
Jemr 16 00015 i003
Jemr 16 00015 i004
Jemr 16 00015 i005
Jemr 16 00015 i006
Jemr 16 00015 i007

Appendix B. Relevant information of each included study

Jemr 16 00015 i008
Jemr 16 00015 i009
Jemr 16 00015 i010
Jemr 16 00015 i011
Jemr 16 00015 i012
Jemr 16 00015 i013
Jemr 16 00015 i014
Jemr 16 00015 i015
Jemr 16 00015 i016
Jemr 16 00015 i017
Jemr 16 00015 i018
Jemr 16 00015 i019
Jemr 16 00015 i020
Jemr 16 00015 i021
Jemr 16 00015 i022
Jemr 16 00015 i023
Jemr 16 00015 i024
Jemr 16 00015 i025
Jemr 16 00015 i026
Jemr 16 00015 i027
Jemr 16 00015 i028
Jemr 16 00015 i029
Jemr 16 00015 i030
Jemr 16 00015 i031
Jemr 16 00015 i032
Jemr 16 00015 i033
Jemr 16 00015 i034
Jemr 16 00015 i035
Jemr 16 00015 i036

References

  1. Abadi, R. v., O. E. Akman, G. E. Arblaster, and R. A. Clement. 2021. Analysing nystagmus waveforms: a computational framework. Scientific Reports 11, 1. [Google Scholar] [CrossRef] [PubMed]
  2. Abadia, B., A. Ferreras, P. Calvo, P. Fogagnolo, M. Figus, and A. B. Pajarin. 2017. Effect of the Eye Tracking System on the Reproducibility of Measurements Obtained with Spectral-domain Optical Coherence Tomography in Glaucoma. Journal of Glaucoma 26, 7: 638–645. [Google Scholar] [CrossRef] [PubMed]
  3. Adams, D. L., J. R. Economides, and J. C. Horton. 2017. Incomitance and Eye Dominance in Intermittent Exotropia. Investigative Ophthalmology & Visual Science 58, 10: 4049–4055. [Google Scholar] [CrossRef]
  4. Aizenman, A. M., and D. M. Levi. 2021. Fixational stability as a measure for the recovery of visual function in amblyopia. Eye Tracking Research and Applications Symposium (ETRA), PartF169260. [Google Scholar] [CrossRef]
  5. Albert, R., A. Patney, D. Luebke, and J. Kim. 2017. Latency Requirements for Foveated Rendering in Virtual Reality. ACM Transactions on Applied Perception 14, 4. [Google Scholar] [CrossRef]
  6. Alberti, C. F., and P. Bex. 2017. Do oculomotor adaptations to a volume scotoma provide functional benefits for binocular vision? Investigative Ophthalmology & Visual Science 58, 8: 4695–4695. [Google Scholar]
  7. Al-Haddad, C., S. Hoyeck, J. Torbey, R. Houry, and R. M. N. Boustany. 2019. Eye tracking abnormalities in school-aged children with strabismus and with and without amblyopia. Journal of Pediatric Ophthalmology and Strabismus 56, 5: 297–304. [Google Scholar] [CrossRef]
  8. Alvarez, T. L., E. H. Kim, C. Yaramothu, and B. Granger-Donetti. 2017. The influence of age on adaptation of disparity vergence and phoria. Vision Research 133: 1–11. [Google Scholar] [CrossRef]
  9. Amit, R., D. Abeles, I. Bar-Gad, and S. Yuval-Greenberg. 2017. Temporal dynamics of saccades explained by a self-paced process. Scientific Reports 7, 1: 1–15. [Google Scholar] [CrossRef]
  10. Asfaw, D. S., P. R. Jones, V. M. Mönter, N. D. Smith, and D. P. Crabb. 2018. Does Glaucoma Alter Eye Movements When Viewing Images of Natural Scenes? A Between-Eye Study. Investigative Ophthalmology & Visual Science 59, 8: 3189–3198. [Google Scholar] [CrossRef]
  11. Awada, A., S. Bakhtiari, C. Legault, C. Odier, and C. C. Pack. 2022. Training with optic flow stimuli promotes recovery in cortical blindness. Restorative Neurology and Neuroscience 40, 1: 1–16. [Google Scholar] [CrossRef]
  12. Axelsson, I., A. Holmblad, and J. Johansson. 2019. Restoring visual capacity after stroke using an intense office-based vision therapy program: Three case reports. Clinical Case Reports 7, 4: 707–713. [Google Scholar] [CrossRef]
  13. Badler, J. B., S. N. J. Watamaniuk, and S. J. Heinen. 2019. A common mechanism modulates saccade timing during pursuit and fixation. Journal of Neurophysiology 122, 5: 1981–1988. [Google Scholar] [CrossRef]
  14. Ballae Ganeshrao, S., A. Jaleel, S. Madicharla, V. Kavya Sri, J. Zakir, C. S. Garudadri, and S. Senthil. 2021. Comparison of Saccadic Eye Movements among the High-tension Glaucoma, Primary Angle-closure Glaucoma, and Normal-tension Glaucoma. Journal of Glaucoma 30, 3: e76–e82. [Google Scholar] [CrossRef] [PubMed]
  15. Barraza-Bernal, M. J., I. v. Ivanov, S. Nill, K. Rifai, S. Trauzettel-Klosinski, and S. Wahl. 2017. Can positions in the visual field with high attentional capabilities be good candidates for a new preferred retinal locus? Vision Research 140: 1–12. [Google Scholar] [CrossRef] [PubMed]
  16. Barraza-Bernal, M. J., K. Rifai, and S. Wahl. 2017. Transfer of an induced preferred retinal locus of fixation to everyday life visual tasks. Undefined 17, 14. [Google Scholar] [CrossRef]
  17. Barsingerhorn, A. D., F. N. Boonstra, and J. Goossens. 2019. Saccade latencies during a preferential looking task and objective scoring of grating acuity in children with and without visual impairments. Acta Ophthalmologica 97, 6: 616–625. [Google Scholar] [CrossRef]
  18. Belyaev, R. v., V. I. Grachev, V. v. Kolesov, G. Y. Menshikova, A. M. Popov, and V. I. Ryabenkov. 2020. Oculomotor reactions in fixations and saccades with visual perception of information. Radioelektronika, Nanosistemy, Informacionnye Tehnologii 12, 2: 263–274. [Google Scholar] [CrossRef]
  19. Blignaut, P., E. J. van Rensburg, and M. Oberholzer. 2019. Visualization and quantification of eye tracking data for the evaluation of oculomotor function. Heliyon 5, 1. [Google Scholar] [CrossRef]
  20. Bowers, N. R., J. Gautier, S. Lin, and A. Roorda. 2021. Fixational eye movements in passive versus active sustained fixation tasks. Journal of Vision 21, 11: 16–16. [Google Scholar] [CrossRef] [PubMed]
  21. Bowman, B., N. C. Ross, P. J. Bex, and T. Arango. 2021. Exploration of dynamic text presentations in bilateral central vision loss. Ophthalmic and Physiological Optics 41, 6: 1183–1197. [Google Scholar] [CrossRef]
  22. Caspi, A., A. Roy, V. Wuyyuru, P. E. Rosendall, J. W. Harper, K. D. Katyal, M. P. Barry, G. Dagnelie, and R. J. Greenberg. 2018. Eye Movement Control in the Argus II Retinal-Prosthesis Enables Reduced Head Movement and Better Localization Precision. Investigative Ophthalmology and Visual Science 59, 2: 792–802. [Google Scholar] [CrossRef]
  23. Cassels, N. K., J. M. Wild, T. H. Margrain, V. Chong, and J. H. Acton. 2018. The use of microperimetry in assessing visual function in age-related macular degeneration. Syrvey of Ophthalmology 63, 1: 40–55. [Google Scholar] [CrossRef]
  24. Chatard, H., L. Tepenier, T. Beydoun, O. Offret, S. Salah, J.-A. Sahel, S. Mohand-Said, and M. P. Bucci. 2019. Effect of Visual Search Training on Saccades in Age-related Macular Degeneration Subjects. Current Aging Science 13, 1: 62–71. [Google Scholar] [CrossRef]
  25. Chaudhary, S., N. Saywell, A. Kumar, and D. Taylor. 2020. Visual Fixations and Motion Sensitivity: Protocol for an Exploratory Study. JMIR Research Protocols 9, 7. [Google Scholar] [CrossRef]
  26. Cheong, A. M., H.-Y. Lam, R. Li, S. Leat, and W. Tsang. 2018. Fast-paced videogame training improves balance under dynamic visual conditions in older adults. Investigative Ophthalmology & Visual Science 59, 9: 5965–5965. [Google Scholar]
  27. Chopra, R., P. J. Mulholland, A. M. Dubis, R. S. Anderson, and P. A. Keane. 2017. Human Factor and Usability Testing of a Binocular Optical Coherence Tomography System. Translational Vision Science & Technology 6, 4. [Google Scholar] [CrossRef]
  28. Chow-Wing-Bom, H., T. M. Dekker, and P. R. Jones. 2020. The worse eye revisited: Evaluating the impact of asymmetric peripheral vision loss on everyday function. Vision Research 169: 49–57. [Google Scholar] [CrossRef] [PubMed]
  29. Coletta, N. J., L. Walker, and F. A. Vera-Diaz. 2018. Refractive Error and Fixation Stability. Investigative Ophthalmology & Visual Science 59, 9: 1091–1091. [Google Scholar]
  30. di Stefano, A. 2002. World optometry: the challenges of leadership for the new millennium. Optometry (St. Louis, Mo.) 73, 6: 339–350. Available online: https://pubmed.ncbi.nlm.nih.gov/12365681/.
  31. Doustkouhi, S. M., P. R. K. Turnbull, and S. C. Dakin. 2020. The effect of refractive error on optokinetic nystagmus. Scientific Reports 10, 1. [Google Scholar] [CrossRef]
  32. Duchowski, A. 2007. Eye Tracking Methodology. In Theory and Practice. Springer-Verlag London Limited: Vol. 2. [Google Scholar]
  33. Economides, J. R., D. L. Adams, and J. C. Horton. 2021. Bilateral Occlusion Reduces the Ocular Deviation in Intermittent Exotropia. Investigative Ophthalmology & Visual Science 62, 1. [Google Scholar] [CrossRef]
  34. Engbert, R., and R. Kliegl. 2003. Microsaccades uncover the orientation of covert attention. Vision Research 43, 9: 1035–1045. [Google Scholar] [CrossRef] [PubMed]
  35. Essig, P., Y. Sauer, and S. Wahl. 2021. Contrast Sensitivity Testing in Healthy and Blurred Vision Conditions Using a Novel Optokinetic Nystagmus Live-Detection Method. Translational Vision Science & Technology 10, 12: 12–12. [Google Scholar] [CrossRef]
  36. Fadzil, N. M., Z. Mohammed, M. M. Shahimin, and N. H. Saliman. 2019. Reading Performance and Compensatory Head Posture in Infantile Nystagmus after Null Zone Training. International Journal of Environmental Research and Public Health 16, 23. [Google Scholar] [CrossRef]
  37. Fang, Y., C. Gill, M. Poletti, and M. Rucci. 2018. Monocular microsaccades: Do they really occur? Journal of Vision 18, 3: 18–18. [Google Scholar] [CrossRef]
  38. Feil, M., B. Moser, and M. Abegg. 2017. The interaction of pupil response with the vergence system. Graefe’s Archive for Clinical and Experimental Ophthalmology=Albrecht von Graefes Archiv Fur Klinische Und Experimentelle Ophthalmologie 255, 11: 2247–2253. [Google Scholar] [CrossRef]
  39. Freedman, A., J. Achtemeier, Y. Baek, and G. E. Legge. 2019. Gaze behavior during navigation with reduced acuity. Experimental Eye Research 183: 20–28. [Google Scholar] [CrossRef]
  40. Fujimoto, S., M. Iwase, and S. Matsuura. 2022. HMD Eye-Tracking Measurement of Miniature Eye Movement Toward VR Image Navigation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13309 LNCS, 203–216. [Google Scholar] [CrossRef]
  41. Gantz, L., and A. Caspi. 2020. Synchronization of a Removable Optical Element with an Eye Tracker: Test Case for Heterophoria Measurement. Translational Vision Science & Technology 9, 7: 40–40. [Google Scholar] [CrossRef]
  42. Gao, Y., and B. A. Sabel. 2017. Microsaccade dysfunction and adaptation in hemianopia after stroke. Restorative Neurology and Neuroscience 35, 4: 365–376. [Google Scholar] [CrossRef]
  43. Garric, C., J. F. Rouland, and Q. Lenoble. 2021. Glaucoma and Computer Use: Do Contrast and Color Enhancements Improve Visual Comfort in Patients? Ophthalmology Glaucoma 4, 5: 531–540. [Google Scholar] [CrossRef]
  44. Giacomelli, G., A. Farini, I. Baldini, M. Raffaelli, G. Bigagli, A. Fossetti, and G. Virgili. 2020. Saccadic movements assessment in eccentric fixation: A study in patients with Stargardt disease. 31, 5, 2556–2562. [Google Scholar] [CrossRef] [PubMed]
  45. Goettker, A., D. I. Braun, and K. R. Gegenfurtner. 2019. Dynamic combination of position and motion information when tracking moving targets. Journal of Vision 19, 7: 2–2. [Google Scholar] [CrossRef] [PubMed]
  46. González, E. G., H. Liu, L. Tarita-Nistor, E. Mandelcorn, and M. Mandelcorn. 2019. Smooth pursuit of amodally completed images. Experimental Eye Research 183: 3–8. [Google Scholar] [CrossRef] [PubMed]
  47. Grillini, A., D. Ombelet, R. S. Soans, and F. W. Cornelissen. 2018. Towards using the spatio-temporal properties of eye movements to classify visual field defects. Eye Tracking Research and Applications Symposium (ETRA). [Google Scholar] [CrossRef]
  48. Hirasawa, K., K. Kobayashi, A. Shibamoto, H. Tobari, Y. Fukuda, and N. Shoji. 2018. Variability in monocular and binocular fixation during standard automated perimetry. PLoS ONE 13, 11. [Google Scholar] [CrossRef]
  49. Hirota, M., T. Hayashi, E. Watanabe, Y. Inoue, and A. Mizota. 2021. Automatic Recording of the Target Location During Smooth Pursuit Eye Movement Testing Using Video-Oculography and Deep Learning-Based Object Detection. Translational Vision Science & Technology 10, 6: 1–1. [Google Scholar] [CrossRef]
  50. Holmqvist, K., and R. Anderson. 2017. Eye tracking: A comprehensive guide to methods, paradigms and measures, Vol. 2.
  51. Hooge, I., K. Holmqvist, and M. Nyström. 2016. The pupil is faster than the corneal reflection (CR): Are video based pupil-CR eye trackers suitable for studying detailed dynamics of eye movements? Vision Research 128: 6–18. [Google Scholar] [CrossRef]
  52. Hotta, K., O. D. A. Prima, T. Imabuchi, and H. Ito. 2019. Compensatory Visual Field Training Based on a Head-Mounted Display Eye Tracker. Communications in Computer and Information Science 1032: 263–268. [Google Scholar] [CrossRef]
  53. Hyona, J., A. Pollatsek, M. Koski, and H. Olkoniemi. 2020. An eye-tracking study of reading long and short novel and lexicalized compound words. JOURNAL OF EYE MOVEMENT RESEARCH 13, 4. [Google Scholar] [CrossRef]
  54. Intoy, J., N. Mostofi, and M. Rucci. 2021. Fast and nonuniform dynamics of perisaccadic vision in the central fovea. Proceedings of the National Academy of Sciences of the United States of America 118, 37. [Google Scholar] [CrossRef]
  55. Ivanchenko, D., F. Schaeffel, and Z. Hafed. 2019. Microvergence fixational eye movements. Investigative Ophthalmology & Visual Science 60, 9: 520–520. [Google Scholar]
  56. Jakobsen, N. S., D. A. Larsen, and T. Bek. 2017. Binocular Fixation Reduces Fixational Eye Movements in the Worst Eye of Patients with Center-Involving Diabetic Macular Edema. Ophthalmic Research 58, 3: 142–149. [Google Scholar] [CrossRef]
  57. Jones, P. R. 2020. An Open-source Static Threshold Perimetry Test Using Remote Eye-tracking (Eyecatcher): Description, Validation, and Preliminary Normative Data. Translational Vision Science & Technology 9, 8. [Google Scholar] [CrossRef]
  58. Jones, P. R., N. D. Smith, W. Bi, and D. P. Crabb. 2019. Portable Perimetry Using Eye-Tracking on a Tablet Computer—A Feasibility Assessment. Translational Vision Science & Technology 8, 1. [Google Scholar] [CrossRef]
  59. Ju, Y. Y., Y. H. Liu, C. H. Cheng, Y. L. Lee, S. T. Chang, C. C. Sun, and H. Y. K. Cheng. 2018. Effects of combat training on visuomotor performance in children aged 9 to 12 years-an eye-tracking study. BMC Pediatrics 18, 1. [Google Scholar] [CrossRef]
  60. Kelly, K. R., C. S. Cheng-Patel, R. M. Jost, Y. Z. Wang, and E. E. Birch. 2019a. Fixation instability during binocular viewing in anisometropic and strabismic children. Experimental Eye Research 183: 29. [Google Scholar] [CrossRef]
  61. Kelly, K. R., C. S. Cheng-Patel, R. M. Jost, Y.-Z. Wang, and E. E. Birch. 2019b. Fixation instability during binocular viewing in anisometropic and strabismic children. Experimental Eye Research 183: 29–37. [Google Scholar] [CrossRef] [PubMed]
  62. Kim, J., Y. Lee, S. Lee, S. Kim, and S. Kwon. 2022. Implementation of Kiosk-Type System Based on Gaze Tracking for Objective Visual Function Examination. Symmetry 14, 3: 499. [Google Scholar] [CrossRef]
  63. König, S. D., and E. A. Buffalo. 2014. A nonparametric method for detecting fixations and saccades using cluster analysis: Removing the need for arbitrary thresholds. Journal of Neuroscience Methods 227: 121–131. [Google Scholar]
  64. Kooiker, M. J. G., H. J. M. Verbunt, J. van der Steen, and J. J. M. Pel. 2019. Combining visual sensory functions and visuospatial orienting functions in children with visual pathology: A longitudinal study. Brain and Development 41, 2: 135–149. [Google Scholar] [CrossRef]
  65. Kwon, S., M. Rolfs, and J. F. Mitchell. 2019. Presaccadic motion integration drives a predictive postsaccadic following response. Journal of Vision 19, 11: 12–12. [Google Scholar] [CrossRef]
  66. Laude, A., D. W. K. Wong, A. P. Yow, M. Mookiah, and T. H. Lim. 2018. Eye gaze tracking and its relationship with visual acuity, central visual field and age-related macular degeneration features. Investigative Ophthalmology & Visual Science 59, 9: 1264–1264. [Google Scholar]
  67. Lauermann, J. L., M. Treder, C. R. Clemens, N. Eter, and F. Alten. 2017. Impact of eye tracking technology on OCT-Angiography imaging in patients with age-related macular degeneration. Investigative Ophthalmology & Visual Science 58, 8. [Google Scholar]
  68. Lee, S. S. Y., A. A. Black, and J. M. Wood. 2017. Effect of glaucoma on eye movement patterns and laboratory-based hazard detection ability. PLoS ONE 12, 6. [Google Scholar] [CrossRef]
  69. Lee, S. S. Y., A. A. Black, and J. M. Wood. 2019. Eye Movements of Drivers with Glaucoma on a Visual Recognition Slide Test. Optometry and Vision Science 96, 7: 484–491. [Google Scholar] [CrossRef] [PubMed]
  70. Leonard, B. T., A. P. Kontos, G. F. Marchetti, M. Zhang, S. R. Eagle, H. M. Reecher, E. S. Bensinger, V. C. Snyder, C. L. Holland, C. K. Sheehy, and E. A. Rossi. 2021. Fixational eye movements following concussion. Journal of Vision 21, 13: 11–12. [Google Scholar] [CrossRef]
  71. Liu, H., Y. Xu, D. Wong, A. P. Yow, A. Laude, and T. H. Lim. 2017. Detecting impaired vision caused by AMD from gaze data. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, July; pp. 3142–3145. [Google Scholar] [CrossRef]
  72. Liu, P., B. N. Nguyen, A. Turpin, and A. M. McKendrick. 2021. Increased Depth, Reduced Extent, and Sharpened Edges of Visual Field Defects Measured by Compass Fundus Perimeter Compared to Humphrey Field Analyzer. Translational Vision Science & Technology 10, 12. [Google Scholar] [CrossRef]
  73. Mankins, J. C. 1995. Technology readiness levels. White Paper. [Google Scholar]
  74. Mao, C., K. Go, Y. Kinoshita, K. Kashiwagi, M. Toyoura, I. Fujishiro, J. Li, and X. Mao. 2021. Different Eye Movement Behaviors Related to Artificial Visual Field Defects-A Pilot Study of Video-Based Perimetry. IEEE Access 9: 77649–77660. [Google Scholar] [CrossRef]
  75. Mestre, C., H. E. Bedell, F. Díaz-Doutón, J. Pujol, and J. Gautier. 2021. Characteristics of saccades during the near point of convergence test. Vision Research 187: 27–40. [Google Scholar] [CrossRef]
  76. Mestre, C., C. Otero, F. Díaz-Doutón, J. Gautier, and J. Pujol. 2018. An automated and objective cover test to measure heterophoria. PLOS ONE 13, 11: e0206674. [Google Scholar] [CrossRef]
  77. Mestre, C., C. Otero, J. Gautier, and J. Pujol. 2017. Does cover test overestimate systematically the phoria values? Investigative Ophthalmology & Visual Science 58, 8: 761–761. [Google Scholar]
  78. Mihara, M., A. Hayashi, K. Kakeue, and R. Tamura. 2020. Longitudinal changes in binocular coordination of smooth pursuit in patients with intermittent exotropia after strabismus surgery. Journal of AAPOS 24, 1: 20.e1–20.e7. [Google Scholar] [CrossRef]
  79. Mooney, S. W. J., N. M. Alam, and G. T. Prusky. 2021. Tracking-Based Interactive Assessment of Saccades, Pursuits, Visual Field, and Contrast Sensitivity in Children With Brain Injury. Frontiers in Human Neuroscience 15. [Google Scholar] [CrossRef]
  80. Mooney, S. W. J., N. Jeremy Hill, M. S. Tuzun, N. M. Alam, J. B. Carmel, and G. T. Prusky. 2018. Curveball: A tool for rapid measurement of contrast sensitivity based on smooth eye movements. Journal of Vision 18, 12: 7–7. [Google Scholar] [CrossRef]
  81. Murata, N., D. Miyamoto, T. Togano, and T. Fukuchi. 2017. Evaluating Silent Reading Performance with an Eye Tracking System in Patients with Glaucoma. PLOS ONE 12, 1. [Google Scholar] [CrossRef]
  82. Murray, I. C., C. Schmoll, A. Perperidis, H. M. Brash, A. D. McTrusty, L. A. Cameron, A. G. Wilkinson, A. O. Mulvihill, B. W. Fleck, and R. A. Minns. 2018. Detection and characterisation of visual field defects using Saccadic Vector Optokinetic Perimetry in children with brain tumours. Eye 32, 10: 1563. [Google Scholar] [CrossRef]
  83. Murray, I., A. Perperidis, L. A. Cameron, A. D. McTrusty, H. M. Brash, A. J. Tatham, P. K. Agarwal, B. W. Fleck, and R. A. Minns. 2017. Comparison of Saccadic Vector Optokinetic Perimetry and Standard Automated Perimetry in Glaucoma. Part I: Threshold Values and Repeatability. Translational Vision Science & Technology 6, 5. [Google Scholar] [CrossRef]
  84. Murray, J., P. Gupta, C. Dulaney, K. Garg, A. G. Shaikh, and F. F. Ghasia. 2022. Effect of Viewing Conditions on Fixation Eye Movements and Eye Alignment in Amblyopia. Investigative Ophthalmology & Visual Science 63, 2: 33–33. [Google Scholar] [CrossRef]
  85. Namaeh, M., M. M. Scheiman, C. Yaramothu, and T. L. Alvarez. 2020. A normative study of objective measures of disparity vergence and saccades in children 9 to 17 years old. Optometry and Vision Science 97, 6: 416–423. [Google Scholar] [CrossRef]
  86. Nanjappa, R., and R. M. McPeek. 2021. Microsaccades and attention in a high-acuity visual alignment task. Journal of Vision 21, 2: 6–6. [Google Scholar] [CrossRef]
  87. Ohlendorf, A., F. Schaeffel, and S. Wahl. 2022. Positions of the horizontal and vertical centre of rotation in eyes with different refractive errors. Ophthalmic & Physiological Optics: The Journal of the British College of Ophthalmic Opticians (Optometrists) 42, 2: 376–383. [Google Scholar] [CrossRef]
  88. Pel, J. J. M., A. C. Boer, and J. van der Steen. 2019. Processing speed in perceptual visual crowding. Journal of Vision 19, 3. [Google Scholar] [CrossRef]
  89. Perperidis, A., A. D. McTrusty, L. A. Cameron, I. C. Murray, H. M. Brash, B. W. Fleck, R. A. Minns, and A. J. Tatham. 2021. The Assessment of Visual Fields in Infants Using Saccadic Vector Optokinetic Perimetry (SVOP): A Feasibility Study. Translational Vision Science & Technology 10, 3: 14–14. [Google Scholar] [CrossRef]
  90. Perrin-Fievez, F., C. Lions, and M. P. Bucci. 2018. Preliminary Study: Impact of Strabismus and Surgery on Eye Movements When Children are Reading. Strabismus 26, 2: 96–104. [Google Scholar] [CrossRef]
  91. Pluzyczka, M. 2018. The First Hundred Years: a History of Eye Tracking as a Research Method. Applied Linguistics Papers 25, 4: 101–116. [Google Scholar] [CrossRef]
  92. Poletti, M., J. Intoy, and M. Rucci. 2020. Accuracy and precision of small saccades. Scientific Reports 10, 1: 1–13. [Google Scholar] [CrossRef]
  93. Pueyo, V., O. Castillo, I. Gonzalez, M. Ortin, T. Perez, D. Gutierrez, E. Prieto, A. Alejandre, and B. Masia. 2020. Oculomotor deficits in children adopted from Eastern Europe. Acta Paediatrica, International Journal of Paediatrics 109, 7: 1439–1444. [Google Scholar] [CrossRef]
  94. Punde, P. A., M. E. Jadhav, and R. R. Manza. 2017. A study of Eye Tracking Technology and its applications. Proceedings-1st International Conference on Intelligent Systems and Information Management, ICISIM 2017, January; pp. 86–90. [Google Scholar] [CrossRef]
  95. Rahn, U., and I. Kozak. 2021. Navigation technology/eye-tracking in ophthalmology: principles, applications and benefits—a narrative review. Annals of Eye Science 6: 6–6. [Google Scholar] [CrossRef]
  96. Ramakrishnan, B., and S. Stevenson. 2020. Effect of alternating flicker occlusion of different frequencies on vergence eye movements. Investigative Ophthalmology & Visual Science 61, 7: 5086–5086. [Google Scholar]
  97. Ramesh, P. v., M. A. Steele, and L. Kiorpes. 2020. Attention in visually typical and amblyopic children. Journal of Vision 20, 3: 1–15. [Google Scholar] [CrossRef]
  98. Ratnam, K., N. R. Bowers, and A. Roorda. 2018. The role of fixational eye movements in maintaining a stable fixation locus. Investigative Ophthalmology & Visual Science 59, 9: 656–656. [Google Scholar]
  99. Raveendran, R. N., A. K. Krishnan, B. Thompson, and R. N. Raveendran. 2020. Reduced fixation stability induced by peripheral viewing does not contribute to crowding. Journal of Vision 20, 10: 1–13. [Google Scholar] [CrossRef]
  100. Reinstein, D. Z., T. J. Archer, R. S. Vida, and G. I. Carp. 2018. Suction stability management in SMILE: Development of a decision tree for managing eye movements and suction loss. Journal of Refractive Surgery 34, 12: 809–816. [Google Scholar] [CrossRef] [PubMed]
  101. Ridder, W. H., P. Yoshinaga, G. Corner, and S. Ridder. 2017. Wilkins Reading Rates in Early and Intermediate AMD Compared to Age Matched Normal Patients. Investigative Ophthalmology & Visual Science 58, 8. [Google Scholar]
  102. Romano, J., and A. Schall. 2014. Eye tracking in User experience design. Elsevier Science & Technology: Available online: https://ebookcentral.proquest.com/lib/universidadcomplutense-ebooks/reader.action?docID=1651794.
  103. Rosengren, W., M. Nyström, B. Hammar, and M. Stridh. 2020. A robust method for calibration of eye tracking data recorded during nystagmus. Behavior Research Methods 52, 1: 36–50. [Google Scholar] [CrossRef] [PubMed]
  104. Ryu, H., and C. Wallraven. 2017. Out of the blue: Effects of blue-filtering lenses on EEG and eye movements during reading. Investigative Ophthalmology & Visual Science 58, 8: 866–866. [Google Scholar]
  105. Ryu, H., and C. Wallraven. 2018. Learning how to recognize objects with a simulated scotoma: an eye-tracking analysis. Investigative Ophthalmology & Visual Science 59, 9. [Google Scholar]
  106. Salvucci, D. D., and J. H. Goldberg. 2000. Identifying fixations and saccades in eye-tracking protocols. 2000 Symposium on Eye Tracking Research & Applications; pp. 71–78. [Google Scholar]
  107. Satgunam, P., K. Nagarajan, and G. Luo. 2021. Children with amblyopia have deficiency in searching real world images. Investigative Ophthalmology & Visual Science 62, 8: 143–143. [Google Scholar]
  108. Schönbach, E. M., M. A. Ibrahim, X. Kong, R. W. Strauss, B. Muñoz, D. G. Birch, J. S. Sunness, S. K. West, and H. P. N. Scholl. 2017. Metrics and Acquisition Modes for Fixation Stability as a Visual Function Biomarker. [CrossRef]
  109. Schweitzer, R., and M. Rolfs. 2020. An adaptive algorithm for fast and reliable online saccade detection. Behavior Research Methods 52: 1122–1139. [Google Scholar] [CrossRef]
  110. Seemiller, E. S., N. L. Port, and T. R. Candy. 2018. The gaze stability of 4-to 10-week-old human infants. Journal of Vision 18, 8. [Google Scholar] [CrossRef]
  111. Senger, C., M. A. Oliveira, C. G. de Moraes, A. Messias, J. S. Paula, Souza, and R. pantojo. 2020. Saccadic movements during an exploratory visual search task in patients with glaucomatous visual field loss. Investigative Ophthalmology & Visual Science 61, 7: 1976–1976. [Google Scholar]
  112. Shanidze, N., S. Heinen, and P. Verghese. 2017. Monocular and Binocular Smooth Pursuit in Central Field Loss. Vision Research 141: 181–190. [Google Scholar] [CrossRef] [PubMed]
  113. Shelchkova, N., C. Tang, and M. Poletti. 2019. Task-driven visual exploration at the foveal scale. Proceedings of the National Academy of Sciences of the United States of America 116, 12: 5811–5818. [Google Scholar] [CrossRef] [PubMed]
  114. Sheynikhovich, D., M. Bécu, C. Wu, and A. Arleo. 2018. Unsupervised detection of microsaccades in a high-noise regime. Journal of Vision 18, 6: 19–19. [Google Scholar] [CrossRef] [PubMed]
  115. Shivdasani, M. N., N. C. Sinclair, L. N. Gillespie, M. A. Petoe, S. A. Titchener, J. B. Fallon, T. Perera, D. Pardinas-Diaz, N. M. Barnes, and P. J. Blamey. 2017. Identification of Characters and Localization of Images Using Direct Multiple-Electrode Stimulation With a Suprachoroidal Retinal Prosthesis. Investigative Ophthalmology & Visual Science 58, 10: 3962–3974. [Google Scholar] [CrossRef]
  116. SR-Research. n.d. EyeLink 1000 Plus.
  117. SR-Research Eye Link. n.d.About Eye Tracking-Fast, Accurate, Reliable Eye Tracking. Available online: Https://Www.Sr-Research.Com/about-Eye-Tracking/.
  118. Taore, A., G. Lobo, P. R. Turnbull, and S. C. Dakin. 2022. Diagnosis of colour vision deficits using eye movements. Scientific Reports 12, 1: 1–14. [Google Scholar] [CrossRef]
  119. Tarita-Nistor, L., E. G. González, T. Brin, M. S. Mandelcorn, A. C. Scherlen, E. D. Mandelcorn, and M. J. Steinbach. 2017. Fixation Stability and Viewing Distance in Patients with AMD. Optometry and Vision Science 94, 2: 239–245. [Google Scholar] [CrossRef]
  120. Tatham, A. J., P. McClean, I. C. Murray, A. D. McTrusty, L. A. Cameron, A. Perperidis, H. M. Brash, B. W. Fleck, and R. A. Minns. 2020. Development of an age-corrected normative database for saccadic vector optokinetic perimetry (SVOP). Journal of Glaucoma 29, 12: 1106–1114. [Google Scholar] [CrossRef]
  121. Tatiyosyan, S. A., K. Rifai, and S. Wahl. 2020. Standalone cooperation-free OKN-based low vision contrast sensitivity estimation in VR-a pilot study. Restorative Neurology and Neuroscience 38, 2: 119–129. [Google Scholar] [CrossRef]
  122. Thomas, N., M. J. Dunn, and J. M. Woodhouse. 2022. Voluntary Flutter Presenting During Ophthalmoscopy: A Case Report. Case Reports in Ophthalmology 13: 286–291. [Google Scholar] [CrossRef]
  123. Thomson, D. 2017. Eye tracking and its clinical application in optometry. Optician 2017, 6: 6045–1. [Google Scholar] [CrossRef]
  124. Titchener, S. A., J. Kvansakul, M. N. Shivdasani, J. B. Fallon, D. A. X. Nayagam, S. B. Epp, C. E. Williams, N. Barnes, W. G. Kentler, M. Kolic, E. K. Baglin, L. N. Ayton, C. J. Abbott, C. D. Luu, P. J. Allen, and M. A. Petoe. 2020. Oculomotor Responses to Dynamic Stimuli in a 44-Channel Suprachoroidal Retinal Prosthesis. Translational Vision Science & Technology 9, 13: 31–31. [Google Scholar] [CrossRef]
  125. Tobii, A. B. n.d. Tobii Media Assets.
  126. Tsirlin, I., L. Colpa, H. C. Goltz, and A. M. F. Wong. 2018. Visual search deficits in amblyopia. Journal of Vision 18, 4: 1–16. [Google Scholar] [CrossRef]
  127. Vater, C., R. Kredel, and E. J. Hossner. 2017. Disentangling vision and attention in multiple-object tracking: How crowding and collisions affect gaze anchoring and dual-task performance. Journal of Vision 17, 5: 21–21. [Google Scholar] [CrossRef] [PubMed]
  128. Vinuela-Navarro, V., J. T. Erichsen, C. Williams, and J. M. Woodhouse. 2017. Saccades and fixations in children with delayed reading skills. Ophthalmic and Physiological Optics 37, 4: 531–541. [Google Scholar] [CrossRef]
  129. Wan, Y., J. Yang, X. Ren, Z. Yu, R. Zhang, and X. Li. 2020. Evaluation of eye movements and visual performance in patients with cataract. Scientific Reports 10, 1. [Google Scholar] [CrossRef]
  130. Wang, K., C. H. Ho, C. Tian, and Y. Zong. 2020. Optical health analysis of visual comfort for bright screen display based on back propagation neural network. Computer Methods and Programs in Biomedicine, 196. [Google Scholar] [CrossRef] [PubMed]
  131. Weaterton, R., S. Tan, J. Adam, H. Kaur, K. Rennie, M. Dunn, S. Ewings, M. Theodorou, D. Osborne, M. Evans, H. Lee, and J. Self. 2020. Beyond Visual Acuity: Development of a Simple Test of the Slow-To-See Phenomenon in Children with Infantile Nystagmus Syndrome. Current Eye Research, 263–270. [Google Scholar] [CrossRef]
  132. Wen, W., S. Wu, S. Wang, L. Zou, Y. Liu, R. Liu, P. Zhang, S. He, and H. Liu. 2018. A Novel Dichoptic Optokinetic Nystagmus Paradigm to Quantify Interocular Suppression in Monocular Amblyopia. Investigative Ophthalmology & Visual Science 59, 12: 4775–4782. [Google Scholar] [CrossRef]
  133. Wertli, J., A. Schötzau, S. Trauzettel-Klosinski, and A. Palmowski-Wolfe. 2020. Feasibility of Eye Movement Recordings with the SMI Tracking Bar in 10-To 11-Year-Old Children Performing a Reading Task. Klinische Monatsblatter Fur Augenheilkunde 237, 4: 510–516. [Google Scholar] [CrossRef]
  134. Wibble, T., J. Engström, and T. Pansell. 2020. Visual and vestibular integration express summative eye movement responses and reveal higher visual acceleration sensitivity than previously described. Investigative Ophthalmology and Visual Science 61, 5. [Google Scholar] [CrossRef]
  135. Wilhelmsen, G. B., M. G. Eide, and M. Felder. 2021. Assessment of eye movements and selected vision function tests in three pupils with albinism: A case study in Tanzania. 1-9 40, 2: 360–368. [Google Scholar] [CrossRef]
  136. Woutersen, K., A. C. Geuzebroek, A. v. van den Berg, and J. Goossens. 2020. Useful Field of View Performance in the Intact Visual Field of Hemianopia Patients. Investigative Ophthalmology & Visual Science 61, 5: 43–43. [Google Scholar] [CrossRef]
  137. Xie, X., F. Song, Y. Liu, S. Wang, and D. Yu. 2021. Study on the effects of display color mode and luminance contrast on visual fatigue. IEEE Access 9: 35915–35923. [Google Scholar] [CrossRef]
  138. Yow, A. P., D. Wong, T. H. Lim, and A. Laude. 2018. Automatic Detection of Preferred Retinal Locus (PRL) for Low Vision Rehabilitation using Oculometrics Analysis∗. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, July; pp. 3954–3957. [Google Scholar] [CrossRef]
  139. Yow, A. P., D. Wong, H. Liu, H. Zhu, I. J.-W. Ong, A. Laude, and T. H. Lim. 2017. Automatic visual impairment detection system for age-related eye diseases through gaze analysis. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); pp. 2450–2453. [Google Scholar] [CrossRef]
  140. Zemblys, R., D. C. Niehorster, and K. Holmqvist. 2019. gazeNet: End-to-end eye-movement event detection with deep neural networks. Behavior Research Methods 51: 840–864. [Google Scholar] [CrossRef]
  141. Zrinscak, O., I. Grubisic, K. Skala, J. S. Herman, T. Kriz, and R. Ivekovic. 2021. Computer based eye tracker for detection of manifest strabismus. Acta Clinica Croatica 60, 4: 683–694. [Google Scholar] [CrossRef]
Jemr 16 00015 g001
Figure 1. PubMed diagram of publications about eye trackers in vision science by year.
Figure 1. PubMed diagram of publications about eye trackers in vision science by year.
Jemr 16 00015 g001
Jemr 16 00015 g002
Figure 2. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart.
Figure 2. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart.
Jemr 16 00015 g002
Table 2. Most commonly used eye-tracker systems.
Table 2. Most commonly used eye-tracker systems.
Jemr 16 00015 i001

Share and Cite

MDPI and ACS Style

González-Vides, L.; Hernández-Verdejo, J.L.; Cañadas-Suárez, P. Eye Tracking in Optometry: A Systematic Review. J. Eye Mov. Res. 2023, 16, 1-55. https://doi.org/10.16910/jemr.16.3.3

AMA Style

González-Vides L, Hernández-Verdejo JL, Cañadas-Suárez P. Eye Tracking in Optometry: A Systematic Review. Journal of Eye Movement Research. 2023; 16(3):1-55. https://doi.org/10.16910/jemr.16.3.3

Chicago/Turabian Style

González-Vides, Leonela, José Luis Hernández-Verdejo, and Pilar Cañadas-Suárez. 2023. "Eye Tracking in Optometry: A Systematic Review" Journal of Eye Movement Research 16, no. 3: 1-55. https://doi.org/10.16910/jemr.16.3.3

APA Style

González-Vides, L., Hernández-Verdejo, J. L., & Cañadas-Suárez, P. (2023). Eye Tracking in Optometry: A Systematic Review. Journal of Eye Movement Research, 16(3), 1-55. https://doi.org/10.16910/jemr.16.3.3

Article Metrics

Back to TopTop