Johari, K., Kim, J.-J., Yow, W. Q., & Tan, U.-X. (2025). A foundation model–based framework for unsupervised gaze anomaly detection. Knowledge-Based Systems, 113774. https://doi.org/10.1016/j.knosys.2025.113774
Abstract:
Traditional gaze analysis methods for online lecture largely depend on predefined average gaze features and self-reported ground-truths, limiting their ability to obtain real-time status in unsupervised settings. To address this, we propose Gaze-READ (Gaze Representative Embedding and Anomaly Detection), a framework that integrates gaze behavior analysis with unsupervised anomaly detection to systematically identify attention shifts caused by external stimuli. Our approach leverages GazeMTM (Masked Time-Series Modeling of Gaze), which employs MOMENT, a time-series foundation model, to extract gaze embeddings that capture temporal dependencies in eye movements. We establish a baseline for normal gaze behavior using a control group (students without distractions) and apply unsupervised clustering to define representative gaze patterns. By comparing this baseline with gaze data from students exposed to distractors, Gaze-READ detects deviations, flagging them as potential indicators of distraction. Experimental results show that MOMENTGET (further pre-trained MOMENT) improves gaze reconstruction, reducing mean squared error (MSE) by at least 28% compared to its pre-trained version and outperforming baseline linear interpolation for oculomotor event representation. Additionally, Gaze-READ achieves a higher clustering silhouette score (0.38) and a lower Davies–Bouldin Index (0.88) than traditional time-series clustering methods, demonstrating its effectiveness in distinguishing gaze patterns. These findings highlight the potential of our framework to enable automated, real-time engagement tracking in online learning environments, offering a scalable solution for identifying attentional shifts without requiring labeled data.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Advanced Manufacturing and Engineering Programmatic Funding Scheme
Grant Reference no. : A18A2b0046