Metadata-driven eye tracking for real-time applications

Abstract

When conducting eye tracking studies, having a mechanism to collect data, build workflows, and validate results in a FAIR (i.e., findable, accessible, interoperable, and reusable) manner, facilitates automation. Given the vast landscape of vendor-specific eye tracking software, adopting FAIR metadata standards for the eye tracking domain is one step towards this. In this paper, we propose an approach to simplify the creation, execution, and validation of eye tracking studies through metadata. Using a metadata format that we developed, we first describe two eye trackers, and two datasets collected using them. Next, we use this metadata to simulate real-time data collection by replaying each dataset. From this replayed data, we analyze eye movements in real-time, and synthesize eye movement data from analytics in real-time. Based on our results, we discuss the utility of metadata in real-time eye tracking studies, and how this idea can be generalized into other applications.