Evaluating Eye Tracking Signal Quality with Real-time Gaze Interaction Simulation

TL;DR

This paper introduces a real-time simulation method to evaluate eye-tracking signal quality using classification algorithms and a novel fixation selection approach, highlighting the impact of algorithm choice on system reliability.

Contribution

It presents a new real-time gaze interaction simulation framework and compares three classification algorithms for assessing eye-tracking signal quality.

Findings

Kalman filter-based algorithm outperforms dispersion threshold in signal quality.

Trigger-event definition impacts detection accuracy and quality assessment.

Significant variability in signal quality across participants emphasizes algorithm importance.

Abstract

We present a real-time gaze-based interaction simulation methodology using an offline dataset to evaluate the eye-tracking signal quality. This study employs three fundamental eye-movement classification algorithms to identify physiological fixations from the eye-tracking data. We introduce the Rank-1 fixation selection approach to identify the most stable fixation period nearest to a target, referred to as the trigger-event. Our evaluation explores how varying constraints impact the definition of trigger-events and evaluates the eye-tracking signal quality of defined trigger-events. Results show that while the dispersion threshold-based algorithm identifies trigger-events more accurately, the Kalman filter-based classification algorithm performs better in eye-tracking signal quality, as demonstrated through a user-centric quality assessment using user- and error-percentile tiers. Despite median user-level performance showing minor differences across algorithms, significant variability in signal quality across participants highlights the importance of algorithm selection to ensure system reliability.