Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologies

• Published in: Expert systems with applications Vol. 166; p. 114037
• Main Authors: Klaib, Ahmad F., Alsrehin, Nawaf O., Melhem, Wasen Y., Bashtawi, Haneen O., Magableh, Aws A.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.03.2021; Elsevier BV
• Subjects: Algorithms; Automobile safety; Choice modeling; Cloud computing; Consumer psychology; Disclaimers; Electrooculography; Eye movements; Eye tracking applications; Eye tracking techniques; Fog computing; Infrared oculography; Internet of Things; Machine learning; Marketing; Scleral coil; Telematics; Tracking; Video oculography; Fog computing; Electrooculography; Cloud computing; Eye tracking applications; Consumer psychology; Eye tracking techniques; Video oculography; Internet of Things; Marketing; Infrared oculography; Machine learning; Choice modeling; Scleral coil
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.114037

•Eye Tracking Algorithms, Techniques, Tools, and Applications.•Scleral search coil, Infrared Oculography, Electro-Oculography, Video Oculography.•Eye Tracking using Machine Learning Approach.•Eye Tracking using Internet of Things and Cloud Computing.•Eye Tracking Techniques and Applications limitations and Future directions. Eye tracking is the process of measuring where one is looking (point of gaze) or the motion of an eye relative to the head. Researchers have developed different algorithms and techniques to automatically track the gaze position and direction, which are helpful in different applications. Research on eye tracking is increasing owing to its ability to facilitate many different tasks, particularly for the elderly or users with special needs. This study aims to explore and review eye tracking concepts, methods, and techniques by further elaborating on efficient and effective modern approaches such as machine learning (ML), Internet of Things (IoT), and cloud computing. These approaches have been in use for more than two decades and are heavily used in the development of recent eye tracking applications. The results of this study indicate that ML and IoT are important aspects in evolving eye tracking applications owing to their ability to learn from existing data, make better decisions, be flexible, and eliminate the need to manually re-calibrate the tracker during the eye tracking process. In addition, they show that eye tracking techniques have more accurate detection results compared with traditional event-detection methods. In addition, various motives and factors in the use of a specific eye tracking technique or application are explored and recommended. Finally, some future directions related to the use of eye tracking in several developed applications are described.