Abstract
This paper presents a study aimed at developing a framework for classifying levels of user satisfaction in Immersive Virtual Environments (IVEs). As an initial step, we conducted an experiment to explore the potential for using machine learning methods to classify levels of Sense of Presence (SOP). Participants performed a task in two virtual environments with varying levels of SOP, and their eye tracking data were analyzed. Our study found that Support Vector Machine (SVM) achieved the best performance based on pupil dilation-based features, with an accuracy of \(79.1\%\). However, relying solely on pupil dilation-based features may not be reliable due to the sensitivity of pupil dilation to light intensity. Eye movement-based features were also analyzed, and Random Forest achieved the best result with an accuracy of \(64.6\%\). In addition, we confirmed that texture resolution affects the perception of SOP by analyzing participants’ IPQ scores. Our results showed that higher resolution leads to a higher perception of SOP. These findings suggest the potential for using machine learning methods to classify levels of SOP based on eye tracking data and provide a positive step towards developing a framework for classifying levels of user satisfaction in IVEs.
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References
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Engbert, R., Rothkegel, L.O.M., Backhaus, D., Trukenbrod, H.A.: Evaluation of velocity-based saccade detection in the SMI-ETG 2W system (2016)
Gao, H., Kasneci, E.: Eye-tracking-based prediction of user experience in VR locomotion using machine learning (2022)
Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998)
Kilmon, C.A., Brown, L., Ghosh, S., Mikitiuk, A.: Immersive virtual reality simulations in nursing education. Nurs. Educ. Perspect. 31(5), 314–317 (2010)
Kim, W., Lee, S., Bovik, A.C.: VR sickness versus VR presence: a statistical prediction model. IEEE Trans. Image Process. 30, 559–571 (2020)
Kleinbaum, D.G., Klein, M.: Logistic Regression. SBH, Springer, New York (2010). https://doi.org/10.1007/978-1-4419-1742-3
Lee, L.H., et al.: All one needs to know about metaverse: a complete survey on technological singularity, virtual ecosystem, and research agenda. arXiv preprint arXiv:2110.05352 (2021)
Loomis, J.M., Blascovich, J.J., Beall, A.C.: Immersive virtual environment technology as a basic research tool in psychology. Behav. Res. Methods Instrum. Comput. 31(4), 557–564 (1999)
Magalie, O., Sameer, J., Philippe, B.: Toward an automatic prediction of the sense of presence in virtual reality environment. In: Proceedings of the 6th International Conference on Human-Agent Interaction, pp. 161–166 (2018)
Pedregosa, F., et al.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)
Roussou, M.: Learning by doing and learning through play: an exploration of interactivity in virtual environments for children. Comput. Entertainment (CIE) 2(1), 10–10 (2004)
Schafer, R.W.: What is a savitzky-golay filter?[lecture notes]. IEEE Signal Process. Mag. 28(4), 111–117 (2011)
Servotte, J.C., et al.: Virtual reality experience: Immersion, sense of presence, and cybersickness. Clin. Simul. Nurs. 38, 35–43 (2020)
Tcha-Tokey, K., Christmann, O., Loup-Escande, E., Richir, S.: Proposition and validation of a questionnaire to measure the user experience in immersive virtual environments. Int. J. Virtual Reality 16(1), 33–48 (2016)
Van Damme, S., Torres Vega, M., De Turck, F.: Enabling user-centric assessment and modelling of immersiveness in multimodal multimedia applications. In: IMX 2022-Doctoral Consortium. pp. 1–10 (2022)
Wienrich, C., Döllinger, N., Kock, S., Schindler, K., Traupe, O.: Assessing user experience in virtual reality – a comparison of different measurements. In: Marcus, A., Wang, W. (eds.) DUXU 2018. LNCS, vol. 10918, pp. 573–589. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91797-9_41
You, S., Kim, J.H., Lee, S., Kamat, V., Robert, L.P., Jr.: Enhancing perceived safety in human-robot collaborative construction using immersive virtual environments. Autom. Constr. 96, 161–170 (2018)
Zheng, L.J., Mountstephens, J., Teo, J.: Four-class emotion classification in virtual reality using pupillometry. J. Big Data 7, 1–9 (2020)
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Pannattee, P., Shimada, S., Yem, V., Nishiuchi, N. (2023). Investigating the Use of Machine Learning Methods for Levels of Sense of Presence Classification Based on Eye Tracking Data. In: Saeed, K., Dvorský, J., Nishiuchi, N., Fukumoto, M. (eds) Computer Information Systems and Industrial Management. CISIM 2023. Lecture Notes in Computer Science, vol 14164. Springer, Cham. https://doi.org/10.1007/978-3-031-42823-4_35
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