License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.COSIT.2022.2
URN: urn:nbn:de:0030-drops-168872
URL: https://drops.dagstuhl.de/opus/volltexte/2022/16887/
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Alinaghi, Negar ; Kattenbeck, Markus ; Giannopoulos, Ioannis

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

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LIPIcs-COSIT-2022-2.pdf (2 MB)


Abstract

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.

BibTeX - Entry

@InProceedings{alinaghi_et_al:LIPIcs.COSIT.2022.2,
  author =	{Alinaghi, Negar and Kattenbeck, Markus and Giannopoulos, Ioannis},
  title =	{{I Can Tell by Your Eyes! Continuous Gaze-Based Turn-Activity Prediction Reveals Spatial Familiarity}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{2:1--2:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-257-0},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{240},
  editor =	{Ishikawa, Toru and Fabrikant, Sara Irina and Winter, Stephan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/16887},
  URN =		{urn:nbn:de:0030-drops-168872},
  doi =		{10.4230/LIPIcs.COSIT.2022.2},
  annote =	{Keywords: Spatial Familiarity, Gaze-based Activity Recognition, Wayfinding, Machine Learning}
}

Keywords: Spatial Familiarity, Gaze-based Activity Recognition, Wayfinding, Machine Learning
Collection: 15th International Conference on Spatial Information Theory (COSIT 2022)
Issue Date: 2022
Date of publication: 22.08.2022
Supplementary Material: Dataset (train and test): https://doi.org/10.48436/f0chy-11p06


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