3 Search Results for "Alinaghi, Negar"


Document
Do You Need Instructions Again? Predicting Wayfinding Instruction Demand

Authors: Negar Alinaghi, Tiffany C. K. Kwok, Peter Kiefer, and Ioannis Giannopoulos

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


Abstract
The demand for instructions during wayfinding, defined as the frequency of requesting instructions for each decision point, can be considered as an important indicator of the internal cognitive processes during wayfinding. This demand can be a consequence of the mental state of feeling lost, being uncertain, mind wandering, having difficulty following the route, etc. Therefore, it can be of great importance for theoretical cognitive studies on human perception of the environment. From an application perspective, this demand can be used as a measure of the effectiveness of the navigation assistance system. It is therefore worthwhile to be able to predict this demand and also to know what factors trigger it. This paper takes a step in this direction by reporting a successful prediction of instruction demand (accuracy of 78.4%) in a real-world wayfinding experiment with 45 participants, and interpreting the environmental, user, instructional, and gaze-related features that caused it.

Cite as

Negar Alinaghi, Tiffany C. K. Kwok, Peter Kiefer, and Ioannis Giannopoulos. Do You Need Instructions Again? Predicting Wayfinding Instruction Demand. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 1:1-1:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@InProceedings{alinaghi_et_al:LIPIcs.GIScience.2023.1,
  author =	{Alinaghi, Negar and Kwok, Tiffany C. K. and Kiefer, Peter and Giannopoulos, Ioannis},
  title =	{{Do You Need Instructions Again? Predicting Wayfinding Instruction Demand}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{1:1--1:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.1},
  URN =		{urn:nbn:de:0030-drops-188963},
  doi =		{10.4230/LIPIcs.GIScience.2023.1},
  annote =	{Keywords: Wayfinding, Navigation Instructions, Urban Computing, Gaze Analysis}
}
Document
I Can Tell by Your Eyes! Continuous Gaze-Based Turn-Activity Prediction Reveals Spatial Familiarity

Authors: Negar Alinaghi, Markus Kattenbeck, and Ioannis Giannopoulos

Published in: LIPIcs, Volume 240, 15th International Conference on Spatial Information Theory (COSIT 2022)


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.

Cite as

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)


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@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-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.2},
  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}
}
Document
Will You Take This Turn? Gaze-Based Turning Activity Recognition During Navigation

Authors: Negar Alinaghi, Markus Kattenbeck, Antonia Golab, and Ioannis Giannopoulos

Published in: LIPIcs, Volume 208, 11th International Conference on Geographic Information Science (GIScience 2021) - Part II


Abstract
Decision making is an integral part of wayfinding and people progressively use navigation systems to facilitate this task. The primary decision, which is also the main source of navigation error, is about the turning activity, i.e., to decide either to turn left or right or continue straight forward. The fundamental step to deal with this error, before applying any preventive approaches, e.g., providing more information, or any compensatory solutions, e.g., pre-calculating alternative routes, could be to predict and recognize the potential turning activity. This paper aims to address this step by predicting the turning decision of pedestrian wayfinders, before the actual action takes place, using primarily gaze-based features. Applying Machine Learning methods, the results of the presented experiment demonstrate an overall accuracy of 91% within three seconds before arriving at a decision point. Beyond the application perspective, our findings also shed light on the cognitive processes of decision making as reflected by the wayfinder’s gaze behaviour: incorporating environmental and user-related factors to the model, results in a noticeable change with respect to the importance of visual search features in turn activity recognition.

Cite as

Negar Alinaghi, Markus Kattenbeck, Antonia Golab, and Ioannis Giannopoulos. Will You Take This Turn? Gaze-Based Turning Activity Recognition During Navigation. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part II. Leibniz International Proceedings in Informatics (LIPIcs), Volume 208, pp. 5:1-5:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


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@InProceedings{alinaghi_et_al:LIPIcs.GIScience.2021.II.5,
  author =	{Alinaghi, Negar and Kattenbeck, Markus and Golab, Antonia and Giannopoulos, Ioannis},
  title =	{{Will You Take This Turn? Gaze-Based Turning Activity Recognition During Navigation}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part II},
  pages =	{5:1--5:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-208-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{208},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.II.5},
  URN =		{urn:nbn:de:0030-drops-147649},
  doi =		{10.4230/LIPIcs.GIScience.2021.II.5},
  annote =	{Keywords: Activity Recognition, Wayfinding, Eye Tracking, Machine Learning}
}
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