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Eye Tracking Technology and Its Applications
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Eye Tracking Technology and Its Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 1217

Special Issue Editor

Dr. Pawel Kasprowski

E-Mail Website
Guest Editor
Department of Applied Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Interests: eye tracking; eye movement processing; signal analysis; pattern recognition; computer vision; biometrics; human–computer interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid advancement of cheap eye trackers and image processing that enable the use of common cameras to track gaze, eye tracking technology has become ubiquitous and accessible. It is our belief that this represents significant potential for future research.

The purpose of this Special Issue is to gather valuable research that utilizes gaze information for different purposes. Today, there is a plethora of applications: medicine, user interfaces, accessibility, psychology, the advertising industry, and artificial intelligence, to name just a few. And, probably, new applications will emerge in the future.

It is important that eye-tracking technology should be easy to use and reliable. Therefore, we would like to invite the contribution of papers that cover the eye tracking methodology and ways for its improvement. Ideas on how to use gaze data and its applications are another important research topic that will be covered by this Special Issue.

The scope of the Special Issue includes, but is not limited to, the following:

  • Collecting eye movement data;
  • Accuracy and precision of data;
  • Calibration of eye movement data signal;
  • Events detection (fixations and saccades);
  • Gaze-based user interfaces;
  • Eye movement modelling;
  • Data mining of eye movement signal;
  • Eye movement-based identification;
  • Improving man–machine interactions;
  • Eye movement applications in testing interface usability;
  • Eye movement in security systems;
  • Usage of eye movement signal in cognitive processes;
  • Recognition of people's intentions based on their eye movement;
  • Miscellaneous eye tracking applications.

Dr. Pawel Kasprowski
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • eye tracking
  • gaze
  • eye movement
  • user interfaces
  • medical applications
  • accessibilty

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Published Papers (2 papers)

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Research

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24 pages, 3925 KB  
Article
Personal Identification Using Eye Movements During Manga Reading: Effects of Stimulus Variation and Template Aging
by Yuichi Wada
Appl. Sci. 2026, 16(7), 3601; https://doi.org/10.3390/app16073601 - 7 Apr 2026
Viewed by 367
Abstract
Eye movements are difficult to observe and replicate, making them a promising yet understudied modality for behavioral biometrics. This study is the first to examine the feasibility of using eye movement patterns during manga reading as a biometric identifier, leveraging the medium’s rich [...] Read more.
Eye movements are difficult to observe and replicate, making them a promising yet understudied modality for behavioral biometrics. This study is the first to examine the feasibility of using eye movement patterns during manga reading as a biometric identifier, leveraging the medium’s rich behavioral data from diverse reading behaviors. Eye movement data from 59 participants were recorded while they read two manga works on a screen. A comprehensive set of gaze features was extracted and evaluated using five machine learning classifiers, among which Random Forest (RF) consistently achieved the best performance. Under constrained experimental conditions, the RF classifier achieved a Rank-1 identification rate of 95.0% and an equal error rate (EER) of 1.9%. Furthermore, this study systematically investigated two critical challenges for practical deployment: stimulus dependency and template aging. Cross-stimulus evaluation revealed substantial performance degradation when training and testing used different manga works, and template aging analysis over an approximately 90-day interval demonstrated notable declines in identification accuracy. These results provide preliminary evidence supporting the potential of natural reading behaviors for biometric continuous authentication systems while highlighting the need for further research into cross-stimulus generalization and temporal stability. Full article
(This article belongs to the Special Issue Eye Tracking Technology and Its Applications)
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Review

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24 pages, 320 KB  
Review
Application of Eye Movement Analysis in Medicine: A Review Across Neurodevelopmental, Neurological, and Neurodegenerative Disorders
by Amnaduny Akhara Nurhasan and Paweł Kasprowski
Appl. Sci. 2026, 16(5), 2548; https://doi.org/10.3390/app16052548 - 6 Mar 2026
Viewed by 499
Abstract
Eye tracking has emerged as a valuable, non-invasive tool for identifying cognitive and motor abnormalities across a wide range of brain-related disorders. Recent studies have explored its utility in neurodevelopmental, neurological, and neurodegenerative conditions. This review synthesizes the findings of studies that apply [...] Read more.
Eye tracking has emerged as a valuable, non-invasive tool for identifying cognitive and motor abnormalities across a wide range of brain-related disorders. Recent studies have explored its utility in neurodevelopmental, neurological, and neurodegenerative conditions. This review synthesizes the findings of studies that apply eye movement analysis including fixation patterns, saccades, scanpaths, and pupil dynamics combined with machine learning (ML) and deep learning (DL) approaches for disease detection and classification. Particular attention is given to the design of eye-tracking tasks, feature extraction strategies, and algorithmic frameworks. Across clinical categories, models such as Support Vector Machines (SVM), random forests (RF), and Convolutional Neural Networks (CNN) have demonstrated promising diagnostic potential, with several studies reporting classification accuracies exceeding 80%, although performance varies depending on the task design, dataset characteristics, and validation methodology. These findings support the potential of eye movement-based biomarkers for early detection and clinical monitoring. Despite encouraging results, current research faces important limitations, including small sample sizes, a lack of standardization, and limited generalizability across populations. To advance clinical translation, future work should emphasize data augmentation, multimodal integration, external validation, and the use of explainable AI (XAI). Overall, eye movement analysis offers a scalable and objective pathway toward improving diagnostic precision in brain-related disorders. Full article
(This article belongs to the Special Issue Eye Tracking Technology and Its Applications)
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