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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

Acknowledgements

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) https://doi.org/10.4230/LIPIcs.COSIT.2022.2

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.

Subject Classification

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

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References

  1. Linda P. Acredolo. The familiarity factor in spatial research. New Directions for Child and Adolescent Development, 1982(15):19-30, 1982. Google Scholar
  2. N. Alinaghi, M. Kattenbeck, A. Golab, and I. Giannopoulos. Will you take this turn? gaze-based turning activity recognition during navigation. In Proc. of the 11th Intl. Conf. on Geographic Information Science (GIScience 2021)-Part II, 2021. Google Scholar
  3. G. L Allen. Spatial abilities, cognitive maps, and wayfinding. Wayfinding Behavior: Cognitive Mapping and Other Spatial Processes, 4680, 1999. Google Scholar
  4. A. Bulling, J. A Ward, H. Gellersen, and G. Tröster. Eye movement analysis for activity recognition using electrooculography. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(4):741-753, 2010. Google Scholar
  5. G. T. Buswell. How people look at pictures: a study of the psychology and perception in art. Univ. Chicago Press, 1935. Google Scholar
  6. H. Couclelis, R. G Golledge, N. Gale, and W. Tobler. Exploring the anchor-point hypothesis of spatial cognition. J.of Environmental Psychology, 7(2):99-122, 1987. Google Scholar
  7. A. Dehghani, O. Sarbishei, T. Glatard, and E. Shihab. A quantitative comparison of overlapping and non-overlapping sliding windows for human activity recognition using inertial sensors. Sensors, 19(22):5026, 2019. Google Scholar
  8. W. Dong, H. Liao, B. Liu, Z. Zhan, H. Liu, L. Meng, and Y. Liu. Comparing pedestrians' gaze behavior in desktop and in real Envs. Cartography & GIScience, 47(5):432-451, 2020. Google Scholar
  9. C. Galdi, H. Wechsler, V. Cantoni, M. Porta, and M. Nappi. Towards demographic categorization using gaze analysis. Pattern Recognition Letters, 82:226-231, 2016. Google Scholar
  10. N. Gale, R. G Golledge, W. C Halperin, and H. Couclelis. Exploring spatial familiarity. The Professional Geographer, 42(3):299-313, 1990. Google Scholar
  11. I. Giannopoulos. Supporting Wayfinding Through Mobile Gaze-Based Interaction. PhD thesis, ETH Zurich, 2016. Google Scholar
  12. L. Gokl, M. Mc Cutchan, B. Mazurkiewicz, P. Fogliaroni, and I Giannopoulos. Towards urban environment familiarity prediction. Adv. in Cartography and GIScience of the ICA, 2(5), 2019. Google Scholar
  13. Antonia Golab, Markus Kattenbeck, Georgios Sarlas, and Ioannis Giannopoulos. It’s also about timing! when do pedestrians want to receive navigation instructions. Spatial Cognition & Computation, pages 1-33, 2021. Google Scholar
  14. R. G Golledge. Place recognition and wayfinding: Making sense of space. Geoforum, 23(2):199-214, 1992. Google Scholar
  15. I. M Harms, B. RD Burdett, and S. G Charlton. The role of route familiarity in traffic participants’ behaviour and transport psychology research: A systematic review. Transportation Research Interdisciplinary Perspectives, 9:100331, 2021. Google Scholar
  16. M. Hegarty and D. Waller. Individual differences in spatial abilities. Cam. Univ. Press, 2005. Google Scholar
  17. J. M Henderson, S. V Shinkareva, J. Wang, S. G Luke, and J. Olejarczyk. Predicting cognitive state from eye movements. PloS One, 8(5):e64937, 2013. Google Scholar
  18. C. Hölscher, T. Meilinger, G. Vrachliotis, M. Brösamle, and M. Knauff. Up the down staircase: Wayfinding strategies in multi-level buildings. J. of Env. Psychology, 26(4):284-299, 2006. Google Scholar
  19. T. Ishikawa and D. R Montello. Spatial knowledge acquisition from direct experience in the environment: Individual differences in the development of metric knowledge and the integration of separately learned places. Cognitive Psychology, 52(2):93-129, 2006. Google Scholar
  20. P. Kiefer, I. Giannopoulos, and M. Raubal. Using eye movements to recognize activities on cartographic maps. Proc of SIGSPATIAL 2013, pages 478-481, 2013. Google Scholar
  21. P. Kiefer, I. Giannopoulos, M. Raubal, and A. Duchowski. Eye tracking for spatial research: Cognition, computation, challenges. Spatial Cognition & Computation, 17(1-2):1-19, 2017. Google Scholar
  22. R. V Levine and A. Norenzayan. The pace of life in 31 countries. J.of Cross-Cultural Psychology, 30(2):178-205, 1999. Google Scholar
  23. R. Li and A. Klippel. Wayfinding behaviors in complex buildings: The impact of environmental legibility and familiarity. Environment and Behavior, 48(3):482-510, 2016. Google Scholar
  24. H. Liao, W. Dong, H. Huang, G. Gartner, and H. Liu. Inferring user tasks in pedestrian navigation from eye movement data in real-world environments. International J.of Geographical Information Science, 33(4):739-763, 2019. Google Scholar
  25. H. Liao, W. Zhao, C. Zhang, W. Dong, and H. Huang. Detecting individuals' spatial familiarity with urban environments using eye movement data. CEUS, 93:101758, 2022. Google Scholar
  26. B. Liu, W. Dong, Z. Zhan, S. Wang, and L. Meng. Differences in the gaze behaviours of pedestrians navigating between regular and irregular road patterns. ISPRS Int J.of Geo-Information, 9(1), 2020. Google Scholar
  27. L. Mathur, M. Spitale, H. Xi, J. Li, and M. J Matarić. Modeling user empathy elicited by a robot storyteller. In 9th Intl. Conf. on Affective Computing and Intelligent Interaction (ACII), pages 1-8, 2021. Google Scholar
  28. F. Meneses and A. Moreira. Using gsm cellid positioning for place discovering. In 2006 Pervasive Health Conf. and Workshops, pages 1-8. IEEE, 2006. Google Scholar
  29. D. R Montello, K. L Lovelace, R. G Golledge, and C. M Self. Sex-related differences and similarities in geographic and environmental spatial abilities. Annals of the Association of American geographers, 89(3):515-534, 1999. Google Scholar
  30. Raffaella Nori and Laura Piccardi. I believe I'm good at orienting myself... But is that true? Cognitive Processing, 16(3):301-307, 2015. Google Scholar
  31. M. J O'Neill. Effects of familiarity and plan complexity on wayfinding in simulated buildings. J.of Environmental Psychology, 12(4):319-327, 1992. Google Scholar
  32. J. Platt et al. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers, 10(3):61-74, 1999. Google Scholar
  33. A. Rousell and A. Zipf. Towards a landmark-based pedestrian navigation service using OSM data. ISPRS Int J.of Geo-Information, 6(3), 2017. Google Scholar
  34. N Savage et al. Seems familiar: An algorithm for inferring spatial familiarity automatically, accessed feb. 5, 2013. Computer Science Department, University of California, Santa Barbara, CA, 2013. Google Scholar
  35. V. Schnitzler, I. Giannopoulos, C. Hölscher, and I. Barisic. The interplay of pedestrian navigation, wayfinding devices, and Enval features in indoor settings. In Proc of ETRA 2016, pages 85-93, 2016. Google Scholar
  36. Y. Shiga, T. Toyama, Y. Utsumi, K. Kise, and A. Dengel. Daily activity recognition combining gaze motion and visual features. Adjunct Proc of UbiComp 2014, pages 1103-1111, 2014. Google Scholar
  37. M. van Haeren and W. Mackaness. The influence of familiarity on route choice: Edinburgh as a case study. In Proc. of GISRUK Conf., Leeds, 2015. Google Scholar
  38. W. Wang. Using location-based social media for ranking individual familiarity with places: a case study with foursquare check-in data. In Progress in Location-Based Services 2014, pages 171-183. Springer, 2015. Google Scholar
  39. F. Wenczel, L. Hepperle, and R. von Stülpnagel. Gaze behavior during incidental and intentional navigation in an outdoor Env. Spatial Cognition & Comp., 17(1-2):121-142, 2017. Google Scholar
  40. J. M Wiener, S. J Büchner, and C. Hölscher. Taxonomy of human wayfinding tasks: A knowledge-based approach. Spatial Cognition & Computation, 9(2):152-165, 2009. Google Scholar
  41. A. L. Yarbus. Eye movements and vision. Springer, 2013. Google Scholar
  42. W. Zhang, X. Zhao, and Z. Li. A comprehensive study of smartphone-based indoor activity recognition via xgboost. IEEE Access, 7:80027-80042, 2019. Google Scholar
  43. Z. Zhou, R. Weibel, and H. Huang. Familiarity-dependent computational modelling of indoor landmark selection for route communication: a ranking approach. International J.of Geographical Information Science, pages 1-33, 2021. Google Scholar
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