I Can Tell by Your Eyes! Continuous Gaze-Based Turn-Activity Prediction Reveals Spatial Familiarity

Authors Negar Alinaghi , Markus Kattenbeck , Ioannis Giannopoulos

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Author Details

Negar Alinaghi
  • Geoinformation, TU Wien, Austria
Markus Kattenbeck
  • Geoinformation, TU Wien, Austria
Ioannis Giannopoulos
  • Geoinformation, TU Wien, Austria
  • Institute of Advanced Research in Artificial Intelligence (IARAI), Wien, Austria


We would like to thank Antonia Golab (Vienna University of Technology) for collecting the valuable data used for this work.

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Negar Alinaghi, Markus Kattenbeck, and Ioannis Giannopoulos. I Can Tell by Your Eyes! Continuous Gaze-Based Turn-Activity Prediction Reveals Spatial Familiarity. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 2:1-2:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


Spatial familiarity plays an essential role in the wayfinding decision-making process. Recent findings in wayfinding activity recognition domain suggest that wayfinders' turning behavior at junctions is strongly influenced by their spatial familiarity. By continuously monitoring wayfinders' turning behavior as reflected in their eye movements during the decision-making period (i.e., immediately after an instruction is received until reaching the corresponding junction for which the instruction was given), we provide evidence that familiar and unfamiliar wayfinders can be distinguished. By applying a pre-trained XGBoost turning activity classifier on gaze data collected in a real-world wayfinding task with 33 participants, our results suggest that familiar and unfamiliar wayfinders show different onset and intensity of turning behavior. These variations are not only present between the two classes -familiar vs. unfamiliar- but also within each class. The differences in turning-behavior within each class may stem from multiple sources, including different levels of familiarity with the environment.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Activity recognition and understanding
  • Computing methodologies → Supervised learning by classification
  • Spatial Familiarity
  • Gaze-based Activity Recognition
  • Wayfinding
  • Machine Learning


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