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Practical Challenges in Using Eye Trackers in the Field

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Part of the Automation, Collaboration, & E-Services book series (ACES,volume 12)

Abstract

Eye-tracking has been used in a wide variety of settings. Eye-tracking as a tool has been found to be useful in psychology, design, marketing and various other fields. With the advent of portable forms of eye-tracking equipment it is now feasible to use them in field conditions and not just in laboratory conditions. Given that they not only provide rich information on visual attention data but also provide insights into mental workload experienced under task conditions, they are now invaluable tools in gathering inputs on design. Increasingly eye-tracking is used as a tool to observe the operator’s performance in various safety critical settings. These are not simulator-based studies but studies in ecologically valid field conditions. In this chapter we carry out a detailed review about the various ways in which eye-trackers have been used in human factor studies in transport systems that are safety critical. The goal is to bring forth the many practical challenges and opportunities in using eye-trackers for human performance studies in field conditions.

Keywords

  • Eye-tracking
  • Field studies
  • Maritime
  • Aviation
  • Sensors
  • Ergonomics and human factors

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References

  1. Funke G et al (2016) Which eye tracker is right for your research? Performance evaluation of several cost variant eye trackers. Proc Hum Factors Ergon Soc Annu Meet 60(1):1240–1244. https://doi.org/10.1177/1541931213601289

    CrossRef  Google Scholar 

  2. Dahlstrom N et al (2009) Fidelity and validity of simulator training. Theor Issues Ergon Sci 10(4):305–314. https://doi.org/10.1080/14639220802368864

    CrossRef  Google Scholar 

  3. Peißl S et al (2018) Eye-tracking measures in aviation: a selective literature review. Int J Aerosp Psychol 28(3–4):98–112. https://doi.org/10.1080/24721840.2018.1514978

    CrossRef  Google Scholar 

  4. Hareide OS et al (2017) Developing a high-speed craft route monitor window. International conference on augmented cognition, Cham. https://doi.org/10.1007/978-3-319-58625-0_33

    CrossRef  Google Scholar 

  5. Hareide OS, Ostnes R (2017) Maritime usability study by analysing eye tracking data. J Navig 70(05):927–943. https://doi.org/10.1017/S0373463317000182

    CrossRef  Google Scholar 

  6. Hareide OS, Ostnes R (2018) Validation of a maritime usability study with eye tracking data. In: Schmorrow DD, Fidopiastis CM (eds) Augmented cognition: users and contexts. Springer International Publishing, Cham, pp. 273–292. https://doi.org/10.1007/978-3-319-91467-1_22

  7. Kassner M et al (2014) Pupil: an open source platform for pervasive eye tracking and mobile gaze-based interaction. In: Adjunct proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing. ACM, New York, NY, USA, pp 1151–1160. https://doi.org/10.1145/2638728.2641695

  8. Forsman F et al (2012) Eye tracking during high speed navigation at sea. J Transp Technol 02(03):277–283. https://doi.org/10.4236/jtts.2012.23030

    CrossRef  Google Scholar 

  9. Frydenberg S et al (2019) Serendipity in the field. Facilitating serendipity in design-driven field studies on ship bridges. Des J 22(sup1):1899–1912. https://doi.org/10.1080/14606925.2019.1594948

  10. Krus K et al (2020) Identifying interesting moments in controllers work video via dimensionality reduction. In: 2020 international conference on artificial intelligence and data analytics for air transportation (AIDA-AT), pp 1–10. https://doi.org/10.1109/AIDA-AT48540.2020.9049170

  11. Babu MD et al (2020) Estimating pilots’ cognitive load from ocular parameters through simulation and in-flight studies. J Eye Mov Res 12(3); Special thematic issue “Eye movements in real and simulated driving and navigation control” No. 3; Special Thematic Issue “Eye movements in real and simulated driving and navigation control” 16

    Google Scholar 

  12. Pignoni G et al (2020) Accounting for effects of variation in luminance in pupillometry for field measurements of cognitive workload. IEEE Sens J 1–1. https://doi.org/10.1109/JSEN.2020.3038291

  13. Dehais F et al, Embedded eye tracker in a real aircraft: new perspectives on pilot/aircraft interaction monitoring 7

    Google Scholar 

  14. Dehais F et al (2020) Monitoring eye movements in real flight conditions for flight training purpose, p. 7. https://doi.org/10.3929/ETHZ-B-000407652

  15. Murthy LRD et al (2020) Eye gaze interface to operate aircraft displays. In: Proceedings—ettc2020. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, Virtual Conference, pp 134–140. https://doi.org/10.5162/ettc2020/4.1

  16. Scannella S et al (2018) Assessment of ocular and physiological metrics to discriminate flight phases in real light aircraft. Hum Factors 14

    Google Scholar 

  17. Grüner M, Ansorge U (2017) Mobile eye tracking during real-world night driving: a selective review of findings and recommendations for future research. J Eye Mov Res 18. https://doi.org/10.16910/jemr.10.2.1

  18. Recarte MA, Nunes LM (2000) Effects of verbal and spatial-imagery tasks on eye fixations while driving. J Exp Psychol Appl 6(1):31–43. https://doi.org/10.1037/1076-898X.6.1.31

    CrossRef  Google Scholar 

  19. Gottlieb W et al (1996) A new scientific instrument for vision in vehicle research. Vis Veh 5:203–210

    Google Scholar 

  20. Kito T et al (1989) Measurements of gaze movements while driving. Percept Mot Skills 68(1):19–25. https://doi.org/10.2466/pms.1989.68.1.19

    CrossRef  Google Scholar 

  21. Muttart JW et al (2011) Glancing and stopping behavior of motorcyclists and car drivers at intersections. Transp Res Rec J Transp Res Board 2265(1):81–88. https://doi.org/10.3141/2265-09

    CrossRef  Google Scholar 

  22. Kandil FI (2010) Car drivers attend to different gaze targets when negotiating closed vs. open bends. J Vis 10(4):1–11. https://doi.org/10.1167/10.4.24

  23. Chattington M et al (2007) Eye–steering coordination in natural driving. Exp Brain Res 180(1):1–14. https://doi.org/10.1007/s00221-006-0839-2

    CrossRef  Google Scholar 

  24. Ahlstrom C et al (2012) Processing of eye/head-tracking data in large-scale naturalistic driving data sets. IEEE Trans Intell Transp Syst 13(2):553–564. https://doi.org/10.1109/TITS.2011.2174786

    CrossRef  Google Scholar 

  25. Pignoni G et al (2019) Trial application of pupillometry for a maritime usability study in field conditions. Necesse 4

    Google Scholar 

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Acknowledgements

We would like to thank NTNU for supporting some of the preliminary studies mentioned in this paper. We would like to thank our colleagues Frode Volden from NTNU for helping with crucial advice related to eye-trackers. We would like to thank Knut Inge Fostervold from UiO, who was kind to provide us with a portable eye tracker for one of the preliminary studies mentioned in this paper.

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Correspondence to Giovanni Pignoni .

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Pignoni, G., Komandur, S. (2023). Practical Challenges in Using Eye Trackers in the Field. In: Duffy, V.G., Ziefle, M., Rau, PL.P., Tseng, M.M. (eds) Human-Automation Interaction. Automation, Collaboration, & E-Services, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-031-10788-7_37

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