The Oculomotor plant mathematical model (OPMM) is a dynamic system that describes a human eye in motion. In this study, we focus on an anatomically inspired homeomorphic model where every component is a mathematical representation of a certain biological phenomenon of a real oculomotor plant. This approach estimates internal state of oculomotor plant from recorded eye movements. In the past, the utility of such models was shown to be useful in biometrics and gaze contingent rendering via eye movement prediction. In previous studies, an implicit underlying assumption was that a set of parameters estimated for a certain subject should remain consistent in time and generalize to unseen data. We note a major drawback of the prior work, as it operated under this assumption without explicit validation. This work creates a quantifiable baseline for the specific OPMM where the generalizability of the model parameters is the foundational property of their estimation.

Photosensor oculography (PSOG) is a promising solution for reducing the computational requirements of eye tracking sensors in wireless virtual and augmented reality platforms. This paper proposes a novel machine learning-based solution for addressing the known performance degradation of PSOG devices in the presence of sensor shifts. Namely, we introduce a convolutional neural network model capable of providing shift-robust end-to-end gaze estimates from the PSOG array output. Moreover, we propose a transfer-learning strategy for reducing model training time. Using a simulated workflow with improved realism, we show that the proposed convolutional model offers improved accuracy over a previously considered multilayer perceptron approach. In addition, we demonstrate that the transfer of initialization weights from pre-trained models can substantially reduce training time for new users. In the end, we provide the discussion regarding the design trade-offs between accuracy, training time, and power consumption among the considered models.