Eye Movement Prediction by Oculomotor Plant Kalman Filter with Brainstem Control

Our work addresses one of the core issues related to Human Computer Interaction (HCI) systems that use eye gaze as an input. This issue is the sensor, transmission and other delays that exist in any eye tracker-based system, reducing its performance. A delay effect can be compensated by an accurate prediction of the eye movement trajectories. This paper introduces a mathematical model of the human eye that uses anatomical properties of the Human Visual System to predict eye movement trajectories. The eye mathematical model is transformed into a Kalman filter form to provide continuous eye position signal prediction during all eye movement types. The model presented in this paper uses brainstem control properties employed during transitions between fast (saccade) and slow (fixations, pursuit) eye movements. Results presented in this paper indicate that the proposed eye model in a Kalman filter form improves the accuracy of eye movement prediction and is capable of a real-time performance. In addition to the HCI systems with the direct eye gaze input, the proposed eye model can be immediately applied for a bit-rate/computational reduction in real-time gaze-contingent systems.