A comparative study of physics-informed and conventional neural networks for predicting on-screen gaze points from eye-tracking data
Konstantinos Roumpas, Eleftheria Lito Michanetzi, Dimosthenis Minas, Angelos Fotopoulos, Michalis Xenos · Expert Systems with Applications · 2025
Physics-Informed Neural Networks (PINNs) take advantage of physical constraints to improve the predictive performance of neural networks. They have demonstrated remarkable success in physics, chemistry, biology, and medicine, sparking interest in their potential applications in other fields, such as Human-Computer Interaction (HCI). Despite their versatility, PINNs are underutilized in certain disciplines due to the challenge of defining Partial Differential Equations (PDEs) that adequately constrain specific problems. This study investigates the application of PINNs to predict fixations captured by an eye-tracking device, employing equations that model head and eye movements. The data for this study were collected during an experiment involving an adaptive user interface for pilots, encompassing a variety of fixation points and rapid glances. The results establish PINNs as a superior method, achieving a mean absolute error (MAE) of 0.61 for horizontal predictions ( X ) and 0.35 for vertical predictions ( Y ), significantly outperforming the conventional neural network (NN). Additionally, the PINN demonstrated stronger predictive accuracy for vertical gaze dynamics, as evidenced by an R Y 2 of 0.91 compared to 0.85 for the NN. These findings underscore the robustness of the PINN approach in modeling gaze points with enhanced precision and reliability. While conventional neural networks perform adequately, the added benefit of physical constraints in PINNs encourages further exploration of their application in non-natural science domains. These promising results point toward the potential of PINNs in creating more adaptive and context-aware systems for various HCI applications.