Parallel Oculomotor Plant Mathematical Model for Large Scale Eye Movement Simulation

TL;DR

This paper introduces a parallel processing architecture for real-time estimation of oculomotor plant characteristics, significantly enhancing speed and accuracy over traditional serial methods, which is crucial for VR/AR eye tracking applications.

Contribution

The paper presents a novel parallel model for large-scale eye movement simulation that outperforms serial implementations in speed and accuracy.

Findings

Parallel implementation improves estimation speed

Enhanced accuracy over serial methods

Suitable for real-time VR/AR eye tracking

Abstract

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.