Parallel Oculomotor Plant Mathematical Model for Large Scale Eye Movement Simulation
This work addresses a domain-specific problem for VR/AR developers and users by enabling faster and more accurate eye movement simulation, though it is incremental as it builds on existing serial methods.
The paper tackled the need for real-time estimation of oculomotor plant characteristics for eye tracking in VR/AR by presenting a parallel processing architecture, which improved speed, accuracy, and throughput compared to a single-threaded implementation.
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.