Abstract
Mixed reality (MR) technology has been attracting attention in the automobile industry and logistics industry for work training and remote work support for newcomers. However, work support using MR technology has the problem that displaying too much information obstructs the user’s field of vision and rather interferes with the work. Therefore, it is necessary to detect the user’s intention and provide only the information that the user wants. In addition, the system should be able to detect naturally from the user’s behavior without interrupting the work, which the user explicitly selects. To solve these problems, in this paper, we use the user’s gaze to determine whether to display content by estimating whether or not they are looking at information. Another problem with gaze-based research in MR work support is that it is difficult to determine which space you are looking at in a space where virtual space and real space intersect. In order to overcome this problem, in this research, we grasp the user’s behavior from the movement of the user’s gaze, and we suggest a way to determine make the user interface (UI) the movement of the gaze that is unlikely to occur during that behavior to see if the user is looking at the UI. As a result of the experiment, we were able to promote the movement of gaze that is different from the characteristics of the movement of gaze during work with the proposed UI movement.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bulling, A., Ward, J.A., Gellersen, H., Tröster, G.: Eye movement analysis for activity recognition using electrooculography. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 741–753 (2010)
De Lope, J., Graña Romay, M.M.: Behavioral activity recognition based on gaze ethograms. Int. J. Neural Syst. 30, 2050025 (2020)
Eduardo, V., Marcus, C., Joshua, N., Augusto, E., Christopher, C.: Motion correlation: selecting objects by matching their movement. ACM Trans. Comput. Hum. Interact. 24(3), 1–35 (2017)
Esteves, A., Shin, Y., Oakley, I.: Comparing selection mechanisms for gaze input techniques in head-mounted displays. Int. J. Hum. Comput. Stud. 139, 102414 (2020)
Fernandez, M., Mathis, F., Khamis, M.: GazeWheels: comparing dwell-time feedback and methods for gaze input. In: Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, pp. 1–6 (2020)
Microsoft: eye tracking on HoloLens 2. https://docs.microsoft.com/en-us/windows/mixed-reality/design/eye-tracking. Accessed 15 Feb 2021
Pfeuffer, K., Mecke, L., Delgado Rodriguez, S., Hassib, M., Maier, H., Alt, F.: Empirical evaluation of gaze-enhanced menus in virtual reality. In: 26th ACM Symposium on Virtual Reality Software and Technology, pp. 1–11 (2020)
Qian, Y.Y., Teather, R.J.: The eyes don’t have it: an empirical comparison of head-based and eye-based selection in virtual reality. In: Proceedings of the 5th Symposium on Spatial User Interaction, pp. 91–98 (2017)
Doniec, R., Sieciński, S., Piaseczna, N., Mocny-Pachońska, K., Lang, M., Szymczyk, J.: The classifier algorithm for recognition of basic driving scenarios. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds.) Information Technology in Biomedicine. AISC, vol. 1186, pp. 359–367. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-49666-1_28
Sidenmark, L., Gellersen, H.: Eye&head: synergetic eye and head movement for gaze pointing and selection. In: Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology, pp. 1161–1174 (2019)
Acknowledgement
This research is supported by JST OPERA(JPMJOP1612), CREST(JPMJCR1882), COI-NEXT(JPMJPF2006) and TMI program (WISE graduate program for lifestyle revolution based on transdisciplinary mobility innovation).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Hayashida, N., Matsuyama, H., Aoki, S., Yonezawa, T., Kawaguchi, N. (2021). A Gaze-Based Unobstructive Information Selection by Context-Aware Moving UI in Mixed Reality. In: Streitz, N., Konomi, S. (eds) Distributed, Ambient and Pervasive Interactions. HCII 2021. Lecture Notes in Computer Science(), vol 12782. Springer, Cham. https://doi.org/10.1007/978-3-030-77015-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-77015-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77014-3
Online ISBN: 978-3-030-77015-0
eBook Packages: Computer ScienceComputer Science (R0)