# Will You Be Aware? Eye Tracking-Based Modeling of Situational Awareness in Augmented Reality

arXiv: https://arxiv.org/abs/2508.05025 · HTML: https://mlsanity.com/paper/2508.05025/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 89 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2306.09364/) (2023)
- [Edge Contraction Pooling for Graph Neural Networks](https://mlsanity.com/paper/1905.10990/) (2019)
- [Understanding Human Gaze Communication by Spatio-Temporal Graph Reasoning](https://mlsanity.com/paper/1909.02144/) (2019)
- [GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality](https://mlsanity.com/paper/2210.07533/) (2022)
- [Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with Machine Learning](https://mlsanity.com/paper/1909.12913/) (2019)
- [Real-Time Multimodal Cognitive Assistant for Emergency Medical Services](https://mlsanity.com/paper/2403.06734/) (2024)
- [Reading Recognition in the Wild](https://mlsanity.com/paper/2505.24848/) (2025)
- [Exploring the Design Space of Optical See-through AR Head-Mounted Displays to Support First Responders in the Field](https://mlsanity.com/paper/2403.04660/) (2024)
- [Alignment Under Pressure: AR-HMD Support Tools for Action Teams](https://mlsanity.com/paper/2502.17295/) (2025)

## Abstract (arXiv)

Augmented Reality (AR) systems, while enhancing task performance through real-time guidance, pose risks of inducing cognitive tunneling-a hyperfocus on virtual content that compromises situational awareness (SA) in safety-critical scenarios. This paper investigates SA in AR-guided cardiopulmonary resuscitation (CPR), where responders must balance effective compressions with vigilance to unpredictable hazards (e.g., patient vomiting). We developed an AR app on a Magic Leap 2 that overlays real-time CPR feedback (compression depth and rate) and conducted a user study with simulated unexpected incidents (e.g., bleeding) to evaluate SA, in which SA metrics were collected via observation and questionnaires administered during freeze-probe events. Eye tracking analysis revealed that higher SA levels were associated with greater saccadic amplitude and velocity, and with reduced proportion and frequency of fixations on virtual content. To predict SA, we propose FixGraphPool, a graph neural network that structures gaze events (fixations, saccades) into spatiotemporal graphs, effectively capturing dynamic attentional patterns. Our model achieved 83.0% accuracy (F1=81.0%), outperforming feature-based machine learning and state-of-the-art time-series models by leveraging domain knowledge and spatial-temporal information encoded in ET data. These findings demonstrate the potential of eye tracking for SA modeling in AR and highlight its utility in designing AR systems that ensure user safety and situational awareness.