Evaluation of the accuracy of human eye movement system identification using step test signals

This study investigates nonlinear dynamic system identification methods to simulate the human eye movement system (EMS), focusing on the accurate representation of transient characteristics derived from step test signals.Integral nonlinear models were applied to capture the nonlinear dynamics and inertial properties of the EMS.Experimental "input-output" data were collected using advanced eye-tracking technology, enabling the identification of multidimensional transient characteristics (MTCs) that describe the EMS's dynamic behavior in response to visual stimuli.The research utilized approximation and compensation methods to develop models based on integro-power series (IPS), while the least squares method (LSM) was applied to construct integro-power polynomial (IPP) models.The compensation method, while less computationally demanding, showed lower accuracy, making it less applicable for tasks requiring high precision.Third-order models exhibited instability in their transient characteristics, limiting their practical use.Second-order models, specifically quadratic IPP models developed with LSM, proved to be the most accurate and computationally efficient.These models provided precise and consistent representations of EMS dynamics, with error rates significantly reduced when using three test signal responses instead of two.This emphasizes the importance of sufficient data in improving model reliability.The findings highlight the suitability of the quadratic IPP model refined with LSM for further investigations.This model offers a robust basis for advancing research into personalized psychophysiological condition assessment through the development of classifiers.Its accuracy and stability make it a valuable tool for exploring state classification methodologies in healthcare, cognitive science, and related domains requiring precise dynamic system simulation.