Special Issue "Latest Research on Eye Tracking Applications" (Applied Sciences, ISSN 2076-3417, IF:2.5), Section "Computing and Artificial Intelligence"
Eye tracking constitutes a powerful technology which can be used for the examination of visual behavior and strategy during the observation of different types of (audio)visual stimuli presented either on a digital monitor, in the physical (real world) space, as well as in a virtual/augmented reality environment. At the same time, eye tracking has the potential to enhance the human–computer interaction experience by providing the ability to manipulate modern digital devices with human eyes. Nowadays, considering the huge amount of eye tracking applications available in various and different scientific/research and professional domains, gaze data collection, analysis, visualization, and modeling face several challenges. Such challenges mainly include the manipulation of big gaze data, the performance of remote (through the internet) experimentation, the semantic extraction of valuable knowledge from collected gaze data, gaze data synchronization during combined implementation with other experimental techniques, and real-time and/or post-experimental data collection using low-cost solutions (including webcams).
Guest Editors:
Full list of published papers:
Editorial:
Krassanakis, V.; David, E.; Le Meur, O. Latest Research on Eye Tracking Applications. Appl. Sci. 2026, 16(10), 4915; https://doi.org/10.3390/app16104915
Milošević, M.; Kovačević, D.; Brozović, M. The Influence of Monochromatic Illustrations on the Attention to Public Health Messages: An Eye-Tracking Study. Appl. Sci. 2024, 14(14), 6003; https://doi.org/10.3390/app14146003.
Wang, Z.; Shen, M.; Huang, Y. Combining Eye-Tracking Technology and Subjective Evaluation to Determine Building Facade Color Combinations and Visual Quality. Appl. Sci. 2024, 14(18), 8227; https://doi.org/10.3390/app14188227.
Katrychuk, D.; Komogortsev, O. Appearance-Based Gaze Estimation as a Benchmark for Eye Image Data Generation Methods. Appl. Sci. 2024, 14(20), 9586; https://doi.org/10.3390/app14209586.
Sáiz-Manzanares, M.; Marticorena-Sánchez, R.; Sáez-García, J.; González-Díez, I. Analysing Virtual Labs Through Integrated Multi-Channel Eye-Tracking Technology: A Proposal for an Explanatory Fit Model. Appl. Sci. 2024, 14(21), 9831; https://doi.org/10.3390/app14219831.
Genova, B.; Bocheva, N.; Hristov, I. Effect of Stimulus Regularities on Eye Movement Characteristics. Appl. Sci. 2024, 14(21), 10055; https://doi.org/10.3390/app142110055.
Gugerell, D.; Gollan, B.; Stolte, M.; Ansorge, U. Studying Pupil-Size Changes as a Function of Task Demands and Emotional Content in a Clinical Interview Situation. Appl. Sci. 2024, 14(24), 11714; https://doi.org/10.3390/app142411714.
Wu, Y.; Zhang, Y.; Zheng, B. Workload Assessment of Operators: Correlation Between NASA-TLX and Pupillary Responses. Appl. Sci. 2024, 14(24), 11975; https://doi.org/10.3390/app142411975.
Ceple, I.; Krauze, L.; Serpa, E.; Svede, A.; Goliskina, V.; Vasiljeva, S.; Kassaliete, E.; Ganebnaya, A.; Volberga, L.; Truksa, R.; Ruza, T.; Krumina, G. Eye Movement Parameters in Children with Reading Difficulties. Appl. Sci. 2025, 15(2), 954; https://doi.org/10.3390/app15020954.
Liu, X.; Zhang, Z.; Dai, J. Evaluating Pupillometry as a Tool for Assessing Facial and Emotional Processing in Nonhuman Primates. Appl. Sci. 2025, 15(6), 3022; https://doi.org/10.3390/app15063022.
Wang, P.; Fu, H. The Influence of Different Visual Elements of High-Density Urban Observation Decks on the Visual Behavior and Place Identity of Tourists and Residents. Appl. Sci. 2025, 15(7), 3875; https://doi.org/10.3390/app15073875.
Andreou, G.; Gkantaki, M. An Eye-Tracking Study on Text Comprehension While Listening to Music: Preliminary Results. Appl. Sci. 2025, 15(7), 3939; https://doi.org/10.3390/app15073939.
Wang, Y.; Li, S.; Rasmussen, Y. Translators’ Allocation of Cognitive Resources in Two Translation Directions: A Study Using Eye-Tracking and Keystroke Logging. Appl. Sci. 2025, 15(8), 4401; https://doi.org/10.3390/app15084401.
Birawo, B.; Kasprowski, P. Performance Analysis of Eye Movement Event Detection Neural Network Models with Different Feature Combinations. Appl. Sci. 2025, 15(11), 6087; https://doi.org/10.3390/app15116087.
Malheiros, B.; Spers, E.; Contreras Castillo, C.; Aroeira, C.; de Lima, L. The Role of Visual Attention and Quality Cues in Consumer Purchase Decisions for Fresh and Cooked Beef: An Eye-Tracking Study. Appl. Sci. 2025, 15(13), 7360; https://doi.org/10.3390/app15137360.
Pluskota, P.; Słupińska, K.; Wawrzyniak, A.; Wąsikowska, B. The Design of Informational and Promotional Messages by Cooperative Banks and Their Perception Among Young Consumers—An Eye-Tracking Analysis Versus Conscious Identification Based on Empirical Research. Appl. Sci. 2025, 15(17), 9635; https://doi.org/10.3390/app15179635.