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

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20 June 2023

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22 June 2023

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Abstract
The rapid development of technology has led to the implementation of data-driven systems whose performance heavily relies on the amount and type of the data itself. In the latest decades, in the fields of bioengineering data management, among others, eye-tracking data has 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 led 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 is developed by using Java programming language, WebSocket protocol, and Representational State Transfer (REST), improving the efficiency in transferring and managing eye-tracking data. 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.
Keywords: 
Subject: Engineering  -   Bioengineering

1. Introduction

In recent decades, technology has become a crucial element of human life, leading to various innovative and convenient advancements across numerous fields, including health [1,2], entertainment [3,4], social media [5,6], physics [7,8] and chemistry [9,10]. While these innovations have positively impacted human life, they also demanded several technological requirements. These requirements, including computational power [11], internet access [12], electricity [13], data [14] and other factors [15,16], have become increasingly crucial in both academia and industry sectors. Data and its management, in particular, became the center node for solving these technological challenges, and their relevance has been further increased by the growth of machine learning methods and AI applications [14,17,18]. However, despite the need for huge data, research applications still lack suitable and effective data collection systems. Moreover, since many studies, especially those regarding a large part of the population moved to mobile applications, real-time data became the strongest constraint to solve [19,20]. These conditions have led researchers to focus on improving and creating novel data collection systems to facilitate the technological advances in the research activity. These data collection systems are widely used in many studies as different as brain signals [21], earthquakes [22], weather conditions [23], etc.
In this context, Representational State Transfer (REST), the most widely used Web-based architecture in both academic literature and industry, has been introduced in 2000 as a Ph.D. thesis by Roy Fielding [24] for leading the design and development of the architecture of an Internet-scale distributed hypermedia system. It facilitates caching of components to reduce user-perceived latency, enforce security, and encapsulate legacy systems [24]. REST employs the Hyper Text Transfer Protocol (HTTP) to enable communication between clients and servers. Its structure provides several advantages, including modifiability and statelessness, which enhanced interoperability [25]. These advantages bring substantial benefits to the management of real-time data.
In addition to the solutions and limitations associated with real-time data management, it is widely recognized that the real-time management of diverse data types presents unique challenges due to their high density and rapid flow rates [26]. Particularly eye-tracking data exemplifies this complexity due to its highly dynamic and rapidly changing nature [27,28,29]. Furthermore, an eye-tracking pattern is an indirect measure of the complex biological system behind it, which requires high-cost computational methods for analysis with models, creating a major problem for the smooth real-time streaming of data.
In this regard, eye tracking can be defined as, is a powerful research tool for studying various topics such as marketing [30], attention [31], perception [32], psychopathology [33], computer vision [34], and decision-making [35,36]. Eye tracking provides insight into the neural mechanisms at the base of exploring strategy of visual stimuli [37]. The eye-tracking technology greatly advanced in recent years, achieving greater precision and accuracy, even in real-world environments [34,38,39]. The history of eye tracking can be traced back to the late 1800s, with improvements in terms of comfort, wearability, and performance for a long time measure of eye movements [36,40,41]. Since then, there have been important advancements that have led to the development of increasingly sophisticated eye-tracking systems [42,43,44].
Although physical eye-tracking devices have improved and become easier to use, the real-world employment of these devices is still not widespread due to their high cost [42,43,44]. This practical issue has prompted researchers to find different solutions, and many eye-tracking models using webcams have been developed [34,39,45]. Many of these models are eligible for implementation locally on the user’s devices and streamed to different platforms via the internet with the help of web servers. Researchers integrate their models into web platforms to reach larger audiences and collect more data. Unfortunately, there is a gap in the literature regarding web-based streaming and storage systems that can be integrated with real-time eye-tracking models.
This study aims to introduce a system that allows the collection, processing, real-time streaming, and storage of eye-tracking data with REST architecture implementation. The manuscript is structured as follows: In Section 1, data necessity, REST, real-time data, and eye-tracking are reported. Section 2 presents Materials and Methods, System Design, Representational State Transfer and Application Programming Interface, WebSocket, Database Server, Docker, WebGazer.js, Hardware Implementation, and Experiment. In Section 3, experimental results are drawn. Section 4 discusses on achieved experimental results compared with those presented in the literature. Lastly, Section 5 provides a conclusion of the entire study and possible future research directions.

2. Materials and Methods

The system’s architecture consists of two software modules, i.e., the frontend and the backend. The former is responsible for interfacing with the user, acquiring information, preprocessing, streaming live data, and transferring to the backend. The latter is the invisible part and includes applications, servers, and databases. In our structure, three different interconnected platforms are designed to collect, process, stream, and store eye movements during an experimental session. These platforms are the Experiment Platform, the Real-Time Results Platform, and the Database Management Platform (see Figure 1). Experiment and Real-Time Results Platforms are located in the frontend whilst the Database Management Platform is located in the backend. Frontend and backend communicate through HTTP protocol. Lastly, data gathered from this study was analyzed by using Python Version 3.10.0 with Matplotlib library version 3.5.3. All details of the system and data flow are reported in the following sessions.

2.1. System Design

The system is designed with three components under two main modules (see Figure 1). The experimental platform (A) and the real-time results platform (B) are in the front-end module. In the backend module, there is a Server (C) component. The frontend development uses JavaScript, HyperText Markup Language (HTML), and Cascading Style Sheets (CSS), which allows the creation of a user-friendly interface. This interface displays the live data stream and the evaluation report of the eye-tracking system. To display the participant’s live video stream, HTML5 video tags are used.
The frontend is specifically designed to integrate of eye-tracking model and provide a clear presentation of its live results. Moreover, data transfer between the Experiment Platform (A) and Real-time Results Platform (B) is carried out via WebSocket. Subsequently, this data is transmitted to the backend server via an HTTP request and stored in the database.
The backend structure is built using Spring Boot, which is a framework of Java language, providing a robust and scalable data processing and management platform. At the end of data collection on the frontend side, the collected data are sent to the backend service by HTTP requests. The backend service aims to process and manage data for the database storage. In addition, the backend provides a data management API that facilitates read and write operations to the SQL database.
Database management involves the use of a SQL database for the efficient storage and management of data. The database provides scalability and ease of retrieval and analysis of stored data. The backend communicates with the database using Java Database Connectivity (JDBC), a Java API designed to access and manage databases. This seamless integration enables effective data handling within the system. Dockerization plays a major role in encapsulating the various components of the system. It involves separating the frontend, backend, and database into separate Docker containers. Each container can be deployed independently, allowing for easy scalability based on the application’s needs. Dockerization also provides a secure and isolated environment for each component, ensuring the stability and security of the overall system.
The system flow can be briefly described as follows: Experiment Platform (A) captures the eye movements and positions of the subject through an embedded model, converts them into coordinates, and sends them to the Real-time Results Platform (B). These two websites communicate to each other via WebSockets and provide data flow by constantly listening to exchanged messages from A to B and vice-versa. The eye-tracking model placed on the experimental platform initiates data collection and its results are then transmitted to a real-time results platform and streamed to the backend via HTTP requests. Following the end of the data collection session, the eye-tracking data, streamed instantaneously on the Real-time Results Platform (B), is sent via HTTP request to the Server (C), which constitutes the last stage of the data flow in the backend.

2.2. Representational State Transfer (REST) and Application Programming Interface (API)

Representational State Transfer (REST) is designed to develop web services based on precise standards and limitations to grant an expandable and adaptable cross-data transaction over the internet [24]. RESTful API (Application Programming Interface) is an interpretation of the REST architecture that provides access to and actions on resources using HTTP. In a RESTful API, the server doesn’t store data about the user between requests; instead, each request has all the data the server needs to process it. RESTful APIs follow a set of constraints, such as client-server architecture, and a consistent interface, among others, to ensure that they are reliable, scalable, and easy to maintain [46,47]. REST has become popular among developers due to its simplicity and flexibility. In addition, RESTful APIs have evolved into a standard for web services development and are actively used by many large companies such as Google, Twitter, etc.

2.3. WebSocket

WebSocket is a communication protocol that belongs to the application layer in the Transmission Control Protocol/Internet Protocol model (TCP/IP) [48]. Due to the popularity and prevalence of HTTP, WebSocket uses HTTP constructs for the initial connection between a client and a server [49] and provides persistent communication so that both the client and the server can send messages at any time. Compared to traditional real-time web communication, the WebSocket protocol saves a lot of network bandwidth and server resources, and the real-time performance is significantly improved [50]. It is helpful for real-time applications such as online games, financial trading platforms, eye tracking, and Internet of Things (IoT) - based applications that support server push technology [51,52].

2.4. Database Server (SQL)

SQL (Structured query language) is a fourth-generation declarative programming language for relational DBMSs (Database management systems) and is a method used to communicate with and manipulate databases [53]. The MySQL database stores and retrieves data via the REST API. The stored procedures and functions are designed as a security layer to perform operations that would receive queries from the API for SQL processing in the database [54].
There are many parameters to consider when evaluating database performance. In particular, NoSQL databases outperform SQL databases regarding write speed and scalability. NoSQL databases perform better when dealing with large scalability requirements and facilitating rapid data updates. [55]. However, SQL databases better manage complex relationships and multiple client scenarios [55]. The characteristics of SQL, structure, and capability to maintain data integrity make them suitable for scenarios involving relational data tables (such as the study carried out). Due to the anticipated availability of multiple user results and relational data in this system, the choice of database system tended

2.5. Docker

Docker is a technology that enables container virtualization, which can be compared to a highly efficient virtual machine due to its lightweight nature [56,57]. It encompasses a modular architecture comprising multiple integral components that interact harmoniously to facilitate the process of "Containerization." At the core of Docker is the Docker Engine, which provides the runtime environment for containers [57]. Docker Images, read-only templates that serve as container building blocks, utilize a layered file system and copy-on-write mechanism for efficient image management [57]. When a Docker Image is instantiated, it becomes a Docker Container, which offers a lightweight and secure execution environment [57]. Docker Containers can be easily created, started, stopped, and deleted, providing flexibility in managing application instances [58]. To facilitate image sharing and distribution, Docker Registries, such as Docker Hub, host a vast collection of pre-built images [59]. Additionally, organizations can establish private registries tailored to their specific image requirements [59]. The modular architecture of Docker, along with its components, enables scalable and flexible application deployment across various environments.

2.6. WebGazer.js

WebGazer.js is a JavaScript-based eye-tracking algorithm. This algorithm allows real-time display of eye-gaze locations on the web using webcams on notebooks and mobile phones [39,60] This tool aims to utilize eye-tracking systems, which are currently only used in controlled environments and experiments, to enable people to use them in their daily lives [39,60]. WebGazer.js consists of two core elements. These are a pupil detector and a gaze estimator. The pupil detector detects the position of the eye and pupil through the webcam. At the same time, the gaze estimator uses regression analysis to estimate where the individual is looking on the screen [39,60]. The gaze estimator applies a regression analysis through a calibration based on mouse clicks and mouse movements.

2.7. Hardware Implementation

In the system, three Dockers virtual environments were used to perform experiments, stream real-time eye movements, and store these data. In order to carry out online experiments, two separate 1 physical AMD Central Processing Units (CPUs), 1 GB Random Access Memory (RAM), and 25 GB Solid State Disk (SSD) hardware were used for the frontend where the eye-tracking model runs and for live feed eye-tracking data. Furthermore, to store the data and manage the backend, 2 physical Intel CPUs, 2 GB RAM, and 25 GB SSD hardware were used. The locations of servers where Dockers are used are located in Frankfurt, Germany for online experiments. The local experiments were conducted with Intel i5 8600k CPUs and 16 GB RAM. Lastly, in both systems, eye-tracking data is collected in X and Y coordinates, while the data time in seconds is stored in Year:Month:Day:Hour:Minute:Second:Millisecond.

2.8. Experiment

Experimental sessions were carried out to assess the reliability of the proposed system architecture, comparing two different scenarios: Local Implementation (LI) and Online Implementation (OI). The local scenario involved configuring the system on the local computer, while the online scenario consisted of configuring the system on the online server.
In order to measure the time delay of both scenarios (LI and OI), the timestamps of each platform were collected during a 100 seconds time window. The delay was computed as subtracting the timestamp value of the experiment platform (A) from the timestamp received on the real-time results platform (B) (B-A), (i.e., arrival time - starting time). Of note, platform (A) sends data to platform (B) at the frequency of 1HZ (see Figure 1).
Moreover, Console.log() function was used to visualize data in the experiment. Specifically, Console.log() is a function that allows the data given into the function to be seen outside the code environment. This function allowed us to capture the precise timestamps indicating the arrival and starting time of data effectively.

3. Results

This study performed a series of statistical analyses to evaluate the difference between the delays of LI and OI. In order to perform the analyses correctly, firstly, the Shapiro-Wilk test was applied to determine whether the delay data were normally distributed. According to the results of the Shapiro-Wilk test, both the LI delay data (Shapiro-Wilk test statistic = 0.370, p < 0.05) and the OI delay data (Shapiro-Wilk test statistic = 0.322, p < 0.05) did not fit a normal distribution. Time difference distributions are shown in Figure 2.
Based on these results, it was concluded that parametric statistical tests could not be used and the Mann-Whitney U test, a non-parametric test, was preferred. The results of the Mann-Whitney U test showed a statistically significant difference between Local and Online latency (U = 794.0, p < 0.05). Table 1 shows Mann-Whitney U test results.
According to Descriptive Statistics, the MAD value for the LI delay was 0.004, the median value was 0.064, the minimum value was 0.020 and the maximum value was 0.660. Similarly, the MAD value for the OI delay was 0.006, the median value was 0.244, the minimum value was 0.101 and the maximum value was 1.123. All results of descriptive statistics are shown in Table 2.
These findings indicate that there is a statistically significant difference between LI and OI delay and that there is a significant difference in their performance.

4. Discussion

The demand for data has witnessed a substantial increase in recent years due to factors such as rapid technological advancements, growing interest in AI from both the private sector and researchers, and the proliferation of diverse research in the literature [61,62,63,64]. However, it is widely acknowledged that data collection systems, expected to keep up with these demands, are falling limited. This study aims to develop a system that facilitates the data collection process for various studies, particularly in the academic domain, while simultaneously enabling real-time observation and streaming of the collected data.
Presently, REST is extensively employed in academic research across various fields, including case generation [65], methodologies [66], biological data [67], and machine learning [68], etc. Furthermore, prominent companies like Google, Amazon, Twitter, and Reddit also utilize this architecture. As part of this study, REST enables instantaneous streaming of the collected data. However, to avoid restricting researchers solely to internet-based usage, the system incorporates the Dockerization technique, allowing for local implementation. Consequently, tests were conducted in local and online (server-based) configurations. A significant difference was found between the time it took for the eye-tracking model data to reach the results page in the locally configured system compared to the same system configured online. Numerous performance bottlenecks such as Internet latency [69], computer configuration [70], and server location [71] present considerable challenges that are difficult to mitigate. Although the latency experienced online is significantly higher than that of the local configuration, it is believed that the experimental online latency is not substantial enough for users to discern [72] (the delay values shown in Figure 2).
The system presented in this study, which is based on several techniques, serves the purpose of real-time streaming and storage of eye-tracking data. However, it is crucial to highlight the flexibility of the proposed system that can be adapted for collecting and analyzing other data types in different experimental settings. For instance, physiological data [73,74], mathematical simulations [75,76], AI models for emotion recognition [77,78], and psychological tests [79,80] are among the potential experiments and data that can be included in this system. Simultaneously, the system allows real-time tracking of users’ eye movements, enabling streaming over the Internet without being limited to a single task.
Furthermore, future studies would involve a larger number of participants and more extensive data, for a deeper understanding of the system’s capabilities and limitations. In addition, the proposed architecture fosters strong collaboration between researchers adopting similar platforms, enabling to an incredibly flexible data exchange and sharing. Moreover, data storage via the Internet is also expected to increase accessibility, thereby encouraging further research and discovery in various fields.

5. Conclusions

This study reported on an approach to data collection and experimentation that demonstrates the intricacies of a multi-purpose system for both online and local applications. This study highlights the fundamental importance of data in scientific endeavors and calls for further exploration of alternative data collection techniques.

Author Contributions

Conceptualization, M.E.S., M.O.S., A.L., A.G., M.C.G., K.P. and M.D.; methodology, A.L., and M.C.G.,; investigation, M.E.S., M.O.S., A.L., M.C.G.; data curation, M.E.S., M.O.S., M.C.G.; writing—original draft preparation, M.C.G., M.O.S., M.E.S. and M.D.; writing—review and editing, A.G., M.C.G., M.O.S., K.P. and A.L.; supervision, A.G., A.L., M.D., and K.P. A. Guazzini and A. Lanata are equally responsible for this study. All authors have read and agreed to the published version of the manuscript.

Funding

Thanks to DigitalOcean (digitalocean.com) and Oliver Mensah for providing us with servers for tests and various other experiments.

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Figure 1. The Tripartite Components of the System Design: Experiment Platform (A), Real-Time Results Platform (B), and Server (C).
Figure 1. The Tripartite Components of the System Design: Experiment Platform (A), Real-Time Results Platform (B), and Server (C).
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Figure 2. Comparison of time differences between the Online and Local systems.
Figure 2. Comparison of time differences between the Online and Local systems.
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Table 1. Mann-Whitney U Test Results Regarding the Local and Online Conditions
Table 1. Mann-Whitney U Test Results Regarding the Local and Online Conditions
Condition U-Statistic P-Value
Local vs Online 794.0 3.317643 × 10 − 25
Table 2. Descriptive Statistics
Table 2. Descriptive Statistics
Condition MAD Median Min Max
Local 0.004 0.064 0.020 0.660
Online 0.006 0.244 0.101 1.123
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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