  The usage of eye tracking sensors is expected to grow in virtual (VR) and
augmented reality (AR) platforms. Provided that users of these platforms
consent to employing captured eye movement signals for authentication and
health assessment, it becomes important to estimate oculomotor plant and brain
function characteristics in real time. This paper shows a path toward that goal
by presenting a parallel processing architecture capable of estimating
oculomotor plant characteristics and comparing its performance to a
single-threaded implementation. Results show that the parallel implementation
improves the speed, accuracy, and throughput of oculomotor plant characteristic
estimation versus the original serial version for both large-scale and
real-time simulation.
