Eye-tracker Signals Processing in System Identification of Human Oculomotor Apparatus using Cloud Technologies

Abstract

The objective of this work is to design and develop a cloud-based web platform based on a new proposed concept of cloud computing organization, that extends the diagnostic capabilities of model-based information technology for assessing neurophysiological states using eye tracking data. Platform is intended to assess human neuro-physiological states using nonlinear dynamic methods for identifying the oculomotor system using eye tracking data. The proposed solution integrates advanced signal processing techniques and combines PaaS and SaaS services, which not only optimizes signal processing workflows but also improves the productivity and efficiency of scientific research. The developed web platform provides integration between eye tracking hardware and server-side architecture, which make possible real-time data collection and processing. The server safely processes large data sets generated by the eye tracking device, which are transmitted for further signal processing and analysis. The main feature of the platform is its ability to process large volumes of neurophysiological data with minimal hardware requirements on the client side, which is made possible by the use of cloud computing technologies. The modular structure allows the platform to be easily scaled to solve signal processing tasks, and also provides secure and isolated execution of scripts in a cloud environment. Compared to other similar services, the platform offers several advantages: it supports efficient work in research and education, supports Python and JavaScript programming languages, and allows the use of software-based signal processing via specially developed GUI interfaces. The inclusion of social features and a high level of abstraction further facilitates collaboration and data sharing, making this platform an innovative tool for research and education.