Photosensor Oculography: Survey and Parametric Analysis of Designs using Model-Based Simulation

TL;DR

This paper reviews photosensor oculography (PSOG) for eye-tracking in AR/VR, using simulation to analyze design trade-offs, accuracy, and effects of sensor shifts, providing insights for optimizing device performance.

Contribution

It introduces a parametric simulation framework to analyze PSOG design variations and their impact on accuracy and cross-talk, highlighting key trade-offs for head-mounted eye-tracking devices.

Findings

Design parameters significantly affect accuracy and cross-talk.

Sensor shifts cause measurable degradation in tracking performance.

Optimal design trade-offs depend on specific application requirements.

Abstract

This paper presents a renewed overview of photosensor oculography (PSOG), an eye-tracking technique based on the principle of using simple photosensors to measure the amount of reflected (usually infrared) light when the eye rotates. Photosensor oculography can provide measurements with high precision, low latency and reduced power consumption, and thus it appears as an attractive option for performing eye-tracking in the emerging head-mounted interaction devices, e.g. augmented and virtual reality (AR/VR) headsets. In our current work we employ an adjustable simulation framework as a common basis for performing an exploratory study of the eye-tracking behavior of different photosensor oculography designs. With the performed experiments we explore the effects from the variation of some basic parameters of the designs on the resulting accuracy and cross-talk, which are crucial characteristics for the seamless operation of human-computer interaction applications based on eye-tracking. Our experimental results reveal the design trade-offs that need to be adopted to tackle the competing conditions that lead to optimum performance of different eye-tracking characteristics. We also present the transformations that arise in the eye-tracking output when sensor shifts occur, and assess the resulting degradation in accuracy for different combinations of eye movements and sensor shifts.