Wearable Eye-tracking for Research: Automated dynamic gaze mapping and accuracy/precision comparisons across devices
Citations Over Time
Abstract
Abstract Wearable eye-trackers offer exciting advantages over screen-based systems, but their use in research settings has been hindered by significant analytic challenges as well as a lack of published performance measures among competing devices on the market. In this article, we address both of these limitations. We describe (and make freely available) an automated analysis pipeline for mapping gaze data from an egocentric coordinate system (i.e. the wearable eye-tracker) to a fixed reference coordinate system (i.e. a target stimulus in the environment). This pipeline allows researchers to study aggregate viewing behavior on a 2D planar target stimulus without restricting the mobility of participants. We also designed a task to directly compare calibration accuracy and precision across 3 popular models of wearable eye-trackers: Pupil Labs 120Hz Binocular glasses, SMI ETG 2 glasses, and the Tobii Pro Glasses 2. Our task encompassed multiple viewing conditions selected to approximate distances and gaze angles typical for short- to mid-range viewing experiments. This work will promote and facilitate the use of wearable eye-trackers for research in naturalistic viewing experiments.
Related Papers
- → Which Eye Tracker Is Right for Your Research? Performance Evaluation of Several Cost Variant Eye Trackers(2016)84 cited
- → Introduction to Eye Tracking(2014)39 cited
- → Basics of Camera-Based Gaze Tracking(2012)11 cited
- → Dhrushti-AI: A multi-screen multi-user eye-tracking system to understand the cognitive behavior of humans in process industries(2023)2 cited
- → Reliable Switching Mechanism for Low Cost Multi-screen Eye Tracking Devices via Deep Recurrent Neural Networks(2018)