Skip to main content

Eye-Tracking Movements—A Comparative Study

  • Conference paper
  • First Online:
Recent Trends in Intelligence Enabled Research (DoSIER 2022)

Abstract

Eye tracking has been a topic of interest in research in recent years because it provides convenience to a wide range of applications. It is acknowledged as an important non-traditional method of human–computer interaction. Eye tracking is a useful tool for determining where and when people devote visual attention to a scene, and it helps to understand cognitive functioning. Nowadays, eye-tracking technology is making its way from the lab to the real world, collecting more data at a faster rate and with a greater variety of data kinds. Eye tracking will become closer to big data if the current trend continues. A real-time model is created using machine learning methodology, which tests a high-accuracy hypothesis. Eye tracking with parameters looks into a participant’s eye movements while presenting them with a variety of options. Using machine learning to analyze eye movements and extract attributes to assess eye behavior. K-nearest neighbor, Naive Bayes, decision trees, and random forests are machine learning algorithms that produce models with improved accuracy. In this paper, we have reviewed different eye-tracking technologies to obtain eye movement parameters and classifiers for categorization, such as machine learning and deep learning toward recognition of cognitive processes involved in learning.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shojaeizadeh, M., Djamasbi, S., Paffenroth, R.C., Trapp, A.C.: Detecting task demand via an eye tracking machine learning system. Decis. Support Syst. 116, 91–101 (2019)

    Article  Google Scholar 

  2. Valliappan, N., Dai, N., Steinberg, E., He, J., Rogers, K., Ramachandran, V., Xu, P., Shojaeizadeh, M., Guo, L., Kohlhoff, K., et al.: Accelerating eye movement research via accurate and affordable smartphone eye tracking. Nat. Commun. 11(1), 1–12 (2020)

    Google Scholar 

  3. Nuraini, A., Murnani, S., Ardiyanto, I., Wibirama, S.: Machine learning in gaze based interaction: a survey of eye movements events detection. In: 2021 International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE), pp. 150–155. IEEE (2021)

    Google Scholar 

  4. Akshay, S., Megha, Y., Shetty, C.B.: Machine learning algorithm to identify eye movement metrics using raw eye tracking data. In: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 949–955. IEEE (2020)

    Google Scholar 

  5. Fikri, M.A., Santosa, P.I., Wibirama, S.: A review on opportunities and challenges of machine learning and deep learning for eye movements classification. In: 2021 IEEE International Biomedical Instrumentation and Technology Conference (IBITeC), pp. 65–70. IEEE (2021)

    Google Scholar 

  6. Roy, A.K., Akhtar, M.N., Mahadevappa, M., Guha, R., Mukherjee, J.: A novel technique to develop cognitive models for ambiguous image identification using eye tracker. IEEE Trans. Affective Comput. 11(1), 63–77 (2017)

    Article  Google Scholar 

  7. Roy, A.K., Nasreen, S., Majumder, D., Mahadevappa, M., Guha, R., Mukhopadhyay, J.: Development of objective evidence in Rorschach ink blot test: an eye tracking study. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1391-1394. IEEE (2019)

    Google Scholar 

  8. Nasreen, S., Roy, A.K., Guha, R.: Exploring ‘little-c’ creativity through eyeparameters. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1078–1081. IEEE (2022)

    Google Scholar 

  9. Çetintaş, D., Firat, T.T.: Eye-tracking analysis with deep learning method. In: 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 512–515. IEEE (2021)

    Google Scholar 

  10. Joseph, A.W., Jeevitha Shree, D., Saluja, K.P.S., Mukhopadhyay, A., Murugesh, R., Biswas, P.: Eye tracking to understand impact of aging on mobile phone applications. In: International Conference on Research into Design, pp. 315–326. Springer (2021)

    Google Scholar 

  11. Feng, Y., Wang, L., Chen, F.: An eye-tracking based evaluation on the effect of far-infrared therapy for relieving visual fatigue. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 313–316. IEEE (2019)

    Google Scholar 

  12. Kokanova, E.S., Lyutyanskaya, M.M., Cherkasova, A.S.: Eye tracking study of reading and sight translation. In: SHS Web of Conferences, vol. 50, p. 01080. EDP Sciences (2018)

    Google Scholar 

  13. Carter, B.T., Luke, S.G.: Best practices in eye tracking research. Int. J. Psychophysiol. 155, 49–62 (2020)

    Article  Google Scholar 

  14. Tamuly, S., Jyotsna, C., Amudha, J.: Tracking eye movements to predict the valence of a scene. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–7. IEEE (2019)

    Google Scholar 

  15. Akinyelu, A.A., Blignaut, P.: Convolutional neural network-based methods for eye gaze estimation: a survey. IEEE Access 8, 142581–142605 (2020)

    Article  Google Scholar 

  16. Koochaki, F., Najafizadeh, L.: Predicting intention through eye gaze patterns. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–4. IEEE (2018)

    Google Scholar 

  17. Arsenovic, M., Sladojevic, S., Stefanovic, D., Anderla, A.: Deep neural network ensemble architecture for eye movements classification. In: 2018 17th International Symposium INFOTEH-JAHORINA (INFOTEH), pp. 1–4. IEEE (2018)

    Google Scholar 

  18. Anisimov, V., Chernozatonsky, K., Pikunov, A., Shedenko, K., Zhigulskaya, D., Arsen, R.: Ml-based classification of eye movement patterns during reading using eye tracking data from an apple ipad device: perspective machine learning algorithm needed for reading quality analytics app on an ipad with built-in eye tracking. In: 2021 International Conference on Cyberworlds (CW), pp. 188–193. IEEE (2021)

    Google Scholar 

  19. Zemblys, R., Niehorster, D.C., Komogortsev, O., Holmqvist, K.: Using machine learning to detect events in eye-tracking data. Behav. Res. Methods 50(1), 160–181 (2018)

    Article  Google Scholar 

  20. Caya, M.V.C., Mendez, B.A.Q., Sanchez, B.J.S., Santos, G.F., Chung, W.Y.: Development of a wearable device for tracking eye movement using pupil lumination comparison algorithm. In: 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM). pp. 1–6. IEEE (2018)

    Google Scholar 

  21. Prasetyo, Y.T., Widyaningrum, R., Lin, C.J.: Eye gaze accuracy in the projection based stereoscopic display as a function of number of fixation, eye movement time, and parallax. In: 2019 IEEE international conference on industrial engineering and engineering management (IEEM), pp. 54–58. IEEE (2019)

    Google Scholar 

  22. Miranda, A.M., Nunes-Pereira, E.J., Baskaran, K., Macedo, A.F.: Eye movements, convergence distance and pupil-size when reading from smartphone, computer, print and tablet. Scand. J. Optometry Vis. Sci. 11(1), 1–5 (2018)

    Article  Google Scholar 

  23. Lin, C.J., Prasetyo, Y.T., Widyaningrum, R.: Eye movement parameters for performance evaluation in projection-based stereoscopic display. J. Eye Movement Res. 11(6) (2018)

    Google Scholar 

  24. Wei, H., Lin, S., Chen, W., Chen, J., Zheng, Y.: Non-invasive image quality assessment based on eye-tracking. In: 2021 7th International Conference on Computer and Communications (ICCC), pp. 1802–1806. IEEE (2021)

    Google Scholar 

  25. Lin, H.J., Chou, L.W., Chang, K.M., Wang, J.F., Chen, S.H., Hendradi, R.: Visual fatigue estimation by eye tracker with regression analysis. J. Sens. 2022 (2022)

    Google Scholar 

  26. Pritalia, G.L., Wibirama, S., Adji, T.B., Kusrohmaniah, S.: Classification of learning styles in multimedia learning using eye-tracking and machine learning. In: 2020 FORTEI-International Conference on Electrical Engineering (FORTEI-ICEE), pp. 145–150. IEEE (2020)

    Google Scholar 

  27. Fuhl, W., Castner, N., Kasneci, E.: Rule-based learning for eye movement type detection. In: Proceedings of the Workshop on Modeling Cognitive Processes from Multimodal Data, pp. 1–6 (2018)

    Google Scholar 

  28. Vortmann, L.M., Knychalla, J., Annerer-Walcher, S., Benedek, M., Putze, F.: Imaging time series of eye tracking data to classify attentional states. Front. Neurosci. 15, 664490 (2021)

    Article  Google Scholar 

  29. Skaramagkas, V., Giannakakis, G., Ktistakis, E., Manousos, D., Karatzanis, I., Tachos, N., Tripoliti, E.E., Marias, K., Fotiadis, D.I., Tsiknakis, M.: Review of eye tracking metrics involved in emotional and cognitive processes. IEEE Rev. Biomed. Eng. (2021)

    Google Scholar 

  30. Oyekunle, R., Bello, O., Jubril, Q., Sikiru, I., Balogun, A.: Usability evaluation using eye-tracking on e-commerce and education domains. J. Inf. Technol. Comput. 1(1), 1–13 (2020)

    Article  Google Scholar 

  31. Sharvashidze, N., Schütz, A.C.: Task-dependent eye-movement patterns in viewing art. J. Eye Movement Res. 13(2) (2020)

    Google Scholar 

  32. Wang, Z., Epps, J., Chen, S.: An investigation of automatic saccade and fixation detection from wearable infrared cameras. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2250–2257. IEEE (2021)

    Google Scholar 

  33. Latifzadeh, K., Amiri, S., Bosaghzadeh, A., Rahimi, M., Ebrahimpour, R.: Evaluating cognitive load of multimedia learning by eye-tracking data analysis. Technol. Educ. J. (TEJ) 15(1), 33–50 (2020)

    Google Scholar 

  34. Potthoff, J., Schienle, A.: Effects of self-esteem on self-viewing: an eye-tracking investigation on mirror gazing. Behav. Sci. 11(12), 164 (2021)

    Article  Google Scholar 

  35. Zhu, Y., Yan, Y., Komogortsev, O.: Hierarchical hmm for eye movement classification. In: European Conference on Computer Vision, pp. 544–554. Springer (2020)

    Google Scholar 

  36. Baharom, N., Aid, S., Amin, M., Wibirama, S., Mikami, O.: Exploring the eye tracking data of human behaviour on consumer merchandise product. J. Adv. Manuf. Technol. (JAMT) 13(2) (2019)

    Google Scholar 

  37. Zandi, A.S., Quddus, A., Prest, L., Comeau, F.J.: Non-intrusive detection of drowsy driving based on eye tracking data. Transp. Res. Rec. 2673(6), 247–257 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Saikat Basu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Saini, S., Roy, A.K., Basu, S. (2023). Eye-Tracking Movements—A Comparative Study. In: Bhattacharyya, S., Das, G., De, S., Mrsic, L. (eds) Recent Trends in Intelligence Enabled Research. DoSIER 2022. Advances in Intelligent Systems and Computing, vol 1446. Springer, Singapore. https://doi.org/10.1007/978-981-99-1472-2_3

Download citation

Publish with us

Policies and ethics