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

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.

Automated summary

The rapid development of technology has resulted in data-driven systems that are heavily dependent on the amount and type of data. Eye-tracking data has become an essential component for various medical, psychological, and engineering research applications, but there is a lack of literature on real-time data collection and management. This study introduces a system developed using Java programming language, WebSocket protocol, and REST to address this gap and improve the efficiency of transferring and managing eye-tracking data. Preliminary results show improved performance in managing real-time data transfer. Overall, this system can benefit the research community by enabling real-time data transfer and storage for more extensive studies using eye-tracking data.