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.

翻译：本文提出了一种利用离线数据集进行实时注视交互模拟的方法，用于评估眼动追踪信号质量。本研究采用三种基础眼动分类算法从眼动数据中识别生理性注视点。我们引入Rank-1注视选择方法，以识别最接近目标（即触发事件）的最稳定注视时段。评估工作探究了不同约束条件如何影响触发事件的定义，并对已定义触发事件的眼动追踪信号质量进行评估。结果表明：虽然基于分散度阈值的算法能更准确地识别触发事件，但基于卡尔曼滤波的分类算法在眼动追踪信号质量方面表现更优——这通过采用用户层级与误差百分位层级的用户中心化质量评估得以验证。尽管在用户中位数性能层面各算法差异较小，但参与者间信号质量的显著差异性凸显了算法选择对确保系统可靠性的重要性。