A Stochastic Nonlinear Dynamical System for Smoothing Noisy Eye Gaze Data

Authors

• Thoa Thieu, The University of Texas Rio Grande Valley
• Roderick Melnik, Wilfrid Laurier University

Document Type

Conference Proceeding

Publication Date

8-21-2026

Abstract

In this study, we address the challenges associated with accurately determining gaze location on a screen, which is often compromised by noise from factors such as eye tracker limitations, calibration drift, ambient lighting changes, and eye blinks. We propose the use of an extended Kalman filter (EKF) to smooth the gaze data collected during eye-tracking experiments and systematically explore the interaction of different system parameters. Our results demonstrate that the EKF significantly reduces noise, leading to a marked improvement in tracking accuracy. Furthermore, we show that our proposed stochastic nonlinear dynamical model aligns well with real experimental data and holds promise for applications in related fields.

Recommended Citation

Thieu, Thoa, and Roderick Melnik. "A stochastic nonlinear dynamical system for smoothing noisy eye gaze data." In International Nonlinear Dynamics Conference, pp. 331-340. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-032-16532-9_29

Publication Title

International Nonlinear Dynamics Conference

DOI

10.1007/978-3-032-16532-9_29