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Investigating the Navigational Behavior of Wheelchair Users in Urban Environments Using Eye Movement Data

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Web and Wireless Geographical Information Systems (W2GIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13912))

Abstract

People with mobility disabilities (PWMD) often struggle with challenges in getting around independently for their daily activities. Mobility is one of the most important life habits which might be constrained by diverse environmental and social obstacles, limiting the social participation of PWMD. Upgrading the social integration of these people is a major challenge in Canada and internationally. Even though the advent of assistive navigation technologies improves the interaction of PWMD with their environments during their mobility, these tools mostly ignore the capabilities, capacities, and specific needs of this population. It is required to better understand PWMD’s navigational behavior in the environment to make these navigation tools adapted to their profile and specific needs. Hence, this research aims at using state-of-the-art technology (i.e., eye-tracking glasses) to explore the navigational behavior of PWMD. To do so, we designed and carried out an experiment in which a wheelchair user wearing eye-tracking glasses navigated a route following the instructions given by Google Maps. Several eye-tracking metrics for the collected eye movement data were computed and analyzed to explore the participant’s visual and mental activities while performing the navigation task. Artificial intelligence was used to automatically assign eye movement data to specific features in the environment during navigation. The preliminary findings of this research show that the highest level of fixation was assigned to the cell phone for receiving the route instructions, distracting thus the participant from his surroundings. In this sense, we have noticed that these route instructions were not sufficient and clear for wheelchair users in some situations. In addition, fixations on sidewalks and crosswalks were the second-highest amount because of the low accessibility level of several parts of the route. Some buildings as landmarks were also eye-catching for the wheelchair user during exploring the environment, and searching for the route, particularly when the route was accessible. In this way, it is required to help the wheelchair user to become aware of information on the accessibility of routes and salient environmental objects in advance to draw more attention to the environment, better orient in the environment, and make sure of following the correct route, therefore, upgrading wheelchair users’ spatial learning and being autonomous.

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Notes

  1. 1.

    People with mobility disabilities.

  2. 2.

    Disability Creation Process model.

  3. 3.

    https://imotions.com/hardware/Tobi-pro-glasses-3/

  4. 4.

    Areas of interest.

  5. 5.

    https://github.com/facebookresearch/detectron2.

  6. 6.

    Region-based Convolutional Neural Networks.

  7. 7.

    Regions of Interest.

  8. 8.

    Common Objects in Context.

  9. 9.

    Points of interest.

References

  1. Levasseur, M., Richard, L., Gauvin, L., Raymond, É.: Inventory and analysis of definitions of social participation found in the aging literature: proposed taxonomy of social activities. Soc. Sci. Med. 71, 2141–2149 (2010). https://doi.org/10.1016/J.SOCSCIMED.2010.09.041

    Article  Google Scholar 

  2. Rimmer, J., Riley, B., Wang, E., Rauworth, A.: Physical activity participation among persons with disabilities: barriers and facilitators. Elsevier (2004)

    Google Scholar 

  3. Ding, D., Parmanto, B., Karimi, H.: Design considerations for a personalized wheelchair navigation system (2007)

    Google Scholar 

  4. Bennett, S., Lee Kirby, R., MacDonald, B.: Wheelchair accessibility: descriptive survey of curb ramps in an urban area. Disabil. Rehabil. Assist. Technol. 4, 17–23 (2009). https://doi.org/10.1080/17483100802542603

    Article  Google Scholar 

  5. Giesbrecht, E., Ripat, J., Cooper, J., Quanbury, A.: Experiences with using a pushrim-activated power-assisted wheelchair for community-based occupations: a qualitative exploration. Can. J. Occup. Therapy 78, 127–136 (2011). https://doi.org/10.2182/cjot.2011.78.2.8

    Article  Google Scholar 

  6. Prescott, M., et al.: An exploration of the navigational behaviours of people who use wheeled mobility devices in unfamiliar pedestrian environments. J. Transp. Heal. 20, 100975 (2021). https://doi.org/10.1016/j.jth.2020.100975

    Article  Google Scholar 

  7. Fougeyrollas, P., Cloutier, R., Bergeron, H., St-Michel, G.: The Quebec classification: Disability creation process (1998)

    Google Scholar 

  8. Matthews, H., Beale, L., Picton, P., Briggs, D.: Modelling access with GIS in urban systems (MAGUS): capturing the experiences of wheelchair users. Area 35, 34–45 (2003). https://doi.org/10.1111/1475-4762.00108

    Article  Google Scholar 

  9. Smith, E.M., Giesbrecht, E.M., Ben Mortenson, W., Miller, W.C.: Prevalence of wheelchair and scooter use among community-dwelling Canadians. Phys. Ther. 96, 1135–1142 (2016). https://doi.org/10.2522/PTJ.20150574

    Article  Google Scholar 

  10. Just, M.A., Carpenter, P.A.: A theory of reading: From eye fixations to comprehension. Psychol. Rev. 87, 329–354 (1980). https://doi.org/10.1037/0033-295X.87.4.329

    Article  Google Scholar 

  11. Gupta, M., et al.: towards more universal wayfinding technologies: navigation preferences across disabilities (2020). https://doi.org/10.1145/3313831.3376581

  12. Mirri, S., Prandi, C., Salomoni, P.: Personalizing Pedestrian Accessible way-finding with mPASS. In: 2016 13th IEEE Annual Consumer Communications Network Conference CCNC 2016, pp. 1119–1124 (2016). https://doi.org/10.1109/CCNC.2016.7444946

  13. Brügger, A., Richter, K.-F., Fabrikant, S.I.: How does navigation system behavior influence human behavior? Cognit. Res.: Principles Implications 4(1), 1–22 (2019). https://doi.org/10.1186/s41235-019-0156-5

    Article  Google Scholar 

  14. Giannopoulos, I., Kiefer, P., Raubal, M.: GazeNav: Gaze-Based Pedestrian Navigation (2015). https://doi.org/10.1145/2785830

  15. Giannopoulos, I., Kiefer, P., Raubal, M.: GeoGazemarks: Providing gaze history for the orientation on small display maps. In: ICMI’12 - Proceedings of the ACM International Conference on Multimodal Interaction, pp. 165–172 (2012). https://doi.org/10.1145/2388676.2388711

  16. Kiefer, P., Giannopoulos, I., Duchowski, A., Raubal, M.: Measuring cognitive load for map tasks through pupil diameter. In: Miller, J.A., O’Sullivan, D., Wiegand, N. (eds.) GIScience 2016. LNCS, vol. 9927, pp. 323–337. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45738-3_21

    Chapter  Google Scholar 

  17. Schwarzkopf, S., Büchner, S.J., Hölscher, C., Konieczny, L.: Perspective tracking in the real world: Gaze angle analysis in a collaborative wayfinding task. Spat. Cogn. Comput. 17, 143–162 (2017). https://doi.org/10.1080/13875868.2016.1226841

    Article  Google Scholar 

  18. Dong, W., Zhan, Z., Liao, H., Meng, L., Liu, J.: Assessing similarities and differences between males and females in visual behaviors in spatial orientation tasks. ISPRS Int. J. Geo-Inf. 9, 115 (2020). https://doi.org/10.3390/ijgi9020115

    Article  Google Scholar 

  19. Wiener, J., Condappa, O.: Do you have to look where you go? Gaze behaviour during spatial decision making (2011)

    Google Scholar 

  20. Liao, H., Dong, W., Huang, H., Gartner, G., Liu, H.: Inferring user tasks in pedestrian navigation from eye movement data in real-world environments. Int. J. Geogr. Inf. Sci. 33, 739–763 (2019). https://doi.org/10.1080/13658816.2018.1482554

    Article  Google Scholar 

  21. Coutrot, A., Hsiao, J.H., Chan, A.B.: Scanpath modeling and classification with hidden Markov models. Behav. Res. Methods 50, 362–379 (2018). https://doi.org/10.3758/S13428-017-0876-8/TABLES/1

    Article  Google Scholar 

  22. Kiefer, P., Giannopoulos, I., Raubal, M., Duchowski, A.: Eye tracking for spatial research: cognition, computation, challenges. Spatial Cognit. Comput. 17, 1–19 (2017). https://doi.org/10.1080/13875868.2016.1254634

    Article  Google Scholar 

  23. Jacob, R.J.K., Karn, K.S.: Eye tracking in human-computer interaction and usability research. ready to deliver the promises. In: The Mind’s Eye: Cognitive and Applied Aspects of Eye Movement Research, pp. 531–553 (2003). https://doi.org/10.1016/B978-044451020-4/50031-1

  24. Zagermann, J., Pfeil, U., Reiterer, H.: Measuring cognitive load using eye tracking technology in visual computing. ACM Int. Conf. Proc. Ser. 78–85 (2016)

    Google Scholar 

  25. Joseph, A.W., Murugesh, R.: Potential eye tracking metrics and indicators to measure cognitive load in human-computer interaction research. J. Sci. Res. 64(1), 168−175 (2020). https://doi.org/10.37398/JSR.2020.640137

  26. Tullis, T., Albert, B.: Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics, Second Edn (2013). https://doi.org/10.1016/C2011-0-00016-9

  27. Clay, V., König, P., König, S.: Eye Tracking in Virtual Reality. J. Eye Mov. Res. 12 (2019)

    Google Scholar 

  28. Chen, F., et al.: Robust Multimodal Cognitive Load Measurement. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31700-7

  29. Ooms, K., Maeyer, P.D., Fack, V., Assche, E.V., Witlox, F.: Investigating the effectiveness of an efficient label placement method using eye movement data. Cartograph. J. 49(3), 234–246 (2012). https://doi.org/10.1179/1743277412Y.0000000010

    Article  Google Scholar 

  30. Goldberg, J., Kotval, X.P.: Eye movement based evaluation of human-computer interfaces (1998)

    Google Scholar 

  31. Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R.: Eye tracking: a comprehensive guide to methods and measures (2011)

    Google Scholar 

  32. Liao, H., Dong, W., Huang, H., Gartner, G.: Inferring user tasks in pedestrian navigation from eye movement data in real-world environments. Taylor Fr. 33, 739–763 (2018). https://doi.org/10.1080/13658816.2018.1482554

    Article  Google Scholar 

  33. Ohm, C., Müller, M., Ludwig, B., Bienk, S.: Where is the Landmark? Eye Tracking Studies in Large-Scale Indoor Environments (2014)

    Google Scholar 

  34. Schrom-Feiertag, H., Settgast, V., Seer, S.: Evaluation of indoor guidance systems using eye tracking in an immersive virtual environment. Spat. Cognit. Comput. 17, 163–183 (2017)

    Article  Google Scholar 

  35. Wang, J., Li, R.: Reassessing Underlying Spatial Relations in Pedestrian Navigation (2018). https://doi.org/10.4018/978-1-5225-5396-0.ch003

  36. Wenczel, F., Hepperle, L., von Stülpnagel, R.: Gaze behavior during incidental and intentional navigation in an outdoor environment. Spat. Cognit. Comput. 17, 121–142 (2017). https://doi.org/10.1080/13875868.2016.1226838

    Article  Google Scholar 

  37. Giannopoulos, I., Kiefer, P., Raubal, M.: Gaze nav: Gaze-based pedestrian navigation. In: MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 337–346. Association for Computing Machinery (2015). https://doi.org/10.1145/2785830.2785873

  38. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell. 42, 386–397 (2017). https://doi.org/10.48550/arxiv.1703.06870

  39. de Carvalho, O.L.F., et al.: Panoptic segmentation meets remote sensing. Remote Sens. 14, 965 (2021). https://doi.org/10.3390/rs14040965

    Article  Google Scholar 

  40. Götze, J., Boye, J.: Resolving Spatial References using Crowdsourced Geographical Data. In: Proceedings of the 20th Nordic Conference of Computational Linguistics, pp. 61–68 (2015)

    Google Scholar 

  41. Ishikawa, T., Fujiwara, H., Imai, O., Okabe, A.: Wayfinding with a GPS-based mobile navigation system: a comparison with maps and direct experience. J. Environ. Psychol. 28, 74–82 (2008). https://doi.org/10.1016/j.jenvp.2007.09.002

    Article  Google Scholar 

  42. Dahmani, L., Bohbot, V.: Habitual use of GPS negatively impacts spatial memory during self-guided navigation. Sci. Rep. 10(1), 6310 (2020)

    Article  Google Scholar 

  43. Schwering, A., Krukar, J., Li, R., Anacta, V.J., Fuest, S.: Wayfinding through orientation. Spat. Cogn. Comput. 17, 273–303 (2017). https://doi.org/10.1080/13875868.2017.1322597

    Article  Google Scholar 

  44. Anacta, V.J.A., Schwering, A., Li, R., Muenzer, S.: Orientation information in wayfinding instructions: evidences from human verbal and visual instructions. GeoJournal 82(3), 567–583 (2016). https://doi.org/10.1007/s10708-016-9703-5

    Article  Google Scholar 

  45. Yesiltepe, D., Conroy Dalton, R., Ozbil Torun, A.: Landmarks in wayfinding: a review of the existing literature. Cogn. Process. 22(3), 369–410 (2021). https://doi.org/10.1007/s10339-021-01012-x

    Article  Google Scholar 

  46. Michon, P.-E., Denis, M.: When and why are visual landmarks used in giving directions? In: Montello, D.R. (ed.) COSIT 2001. LNCS, vol. 2205, pp. 292–305. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45424-1_20

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Center for Research in Geospatial Information and Intelligence, Department of Geomatics Sciences, Université Laval, 1055, Avenue du Séminaire, Québec, Canada

    Sanaz Azimi & Mir Abolfazl Mostafavi

  2. Interdisciplinary Research Center for Rehabilitation and Social Integration, Université Laval, Québec, QC, Canada

    Krista Lynn Best

  3. Laboratoire de Psychologie Et d’Ergonomie Appliquées (LaPEA), Université Gustave Eiffel, Paris, France

    Aurélie Dommes

Authors
  1. Sanaz Azimi
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  2. Mir Abolfazl Mostafavi
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  3. Krista Lynn Best
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  4. Aurélie Dommes
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Corresponding author

Correspondence to Mir Abolfazl Mostafavi .

Editor information

Editors and Affiliations

  1. Université Laval, Québec, QC, Canada

    Mir Abolfazl Mostafavi

  2. INSA, Saint Etienne du Rouvray Cedex, France

    Géraldine Del Mondo

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Azimi, S., Mostafavi, M.A., Best, K.L., Dommes, A. (2023). Investigating the Navigational Behavior of Wheelchair Users in Urban Environments Using Eye Movement Data. In: Mostafavi, M.A., Del Mondo, G. (eds) Web and Wireless Geographical Information Systems. W2GIS 2023. Lecture Notes in Computer Science, vol 13912. Springer, Cham. https://doi.org/10.1007/978-3-031-34612-5_4

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  • DOI: https://doi.org/10.1007/978-3-031-34612-5_4

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