Analysis of the accuracy of simulating the human eye movement system based on Volterra models

Abstract

Integral nonlinear models are used to simulate the human eye movement system (EMS) while accounting for its nonlinear dynamics and inertial properties. Multidimensional transient characteristics (MTCs) of the EMS were identified based on experimental input-output data obtained from eye-tracking responses to visual test stimuli. These transient characteristics include first-order and diagonal cross-sections up to the second and third orders of MTCs. The study aimed to evaluate the accuracy of EMS simulation models by analyzing the calculation errors of transient characteristics using nonlinear dynamic identification methods based on Volterra integro-power series (IPS) and integro-power polynomial (IPP). Computational methods, including the least squares method (LSM), approximation, and compensation, were used to derive the models. Models developed using the LSM and approximation methods produced consistent transient characteristics when the same test signals were applied, highlighting the convergence of the Volterra series within the identified region. The findings showed that increasing the number of test signals enhanced the accuracy of the EMS models. Quadratic models were identified as the most reliable, providing a balance between precision and computational efficiency. Cubic models closely matched EMS responses but exhibited instability in their transient characteristics, making them less practical for EMS application. The compensation method, while computationally less demanding, proved unsuitable for tasks requiring high accuracy due to significant errors in the resulting models. Quadratic IPP models developed with LSM based on three response datasets are recommended for future studies, as they provide a stable and precise framework for modeling EMS dynamics and exploring psychophysiological state assessment.