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Inicio  /  Algorithms  /  Vol: 16 Par: 7 (2023)  /  Artículo
ARTÍCULO
TITULO

Design of Cloud-Based Real-Time Eye-Tracking Monitoring and Storage System

Mustafa Can Gursesli    
Mehmet Emin Selek    
Mustafa Oktay Samur    
Mirko Duradoni    
Kyoungju Park    
Andrea Guazzini and Antonio Lanatà    

Resumen

The rapid development of technology has led to the implementation of data-driven systems whose performance heavily relies on the amount and type of data. In the latest decades, in the field of bioengineering data management, among others, eye-tracking data have become one of the most interesting and essential components for many medical, psychological, and engineering research applications. However, despite the large usage of eye-tracking data in many studies and applications, a strong gap is still present in the literature regarding real-time data collection and management, which leads to strong constraints for the reliability and accuracy of on-time results. To address this gap, this study aims to introduce a system that enables the collection, processing, real-time streaming, and storage of eye-tracking data. The system was developed using the Java programming language, WebSocket protocol, and Representational State Transfer (REST), improving the efficiency in transferring and managing eye-tracking data. The results were computed in two test conditions, i.e., local and online scenarios, within a time window of 100 seconds. The experiments conducted for this study were carried out by comparing the time delay between two different scenarios, even if preliminary results showed a significantly improved performance of data management systems in managing real-time data transfer. Overall, this system can significantly benefit the research community by providing real-time data transfer and storing the data, enabling more extensive studies using eye-tracking data.

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