GazeDock: Gaze-Only Menu Selection in Virtual Reality using Auto-Triggering Peripheral Menu

Gaze-only input techniques in VR face the challenge of avoiding false triggering due to continuous eye tracking while maintaining interaction performance. In this paper, we proposed GazeDock, a technique for enabling fast and robust gaze-based menu selection in VR. GazeDock features a view-fixed per...

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Vydané v:Proceedings (IEEE Conference on Virtual Reality and 3D User Interfaces. Online) s. 832 - 842
Hlavní autori: Yi, Xin, Lu, Yiqin, Cai, Ziyin, Wu, Zihan, Wang, Yuntao, Shi, Yuanchun
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.03.2022
Predmet:
Games
Gaze tracking
Human-centered computing-Human computer interaction (HCI)- Interaction paradigms-Virtual reality
Human-centered computing-Human computer interaction (HCI)-HCI design and evaluation methods-User studies
Layout
Navigation
Three-dimensional displays
User interfaces
Virtual reality
ISSN:2642-5254
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Shrnutí:Gaze-only input techniques in VR face the challenge of avoiding false triggering due to continuous eye tracking while maintaining interaction performance. In this paper, we proposed GazeDock, a technique for enabling fast and robust gaze-based menu selection in VR. GazeDock features a view-fixed peripheral menu layout that automatically triggers appearing and selection when the user's gaze approaches and leaves the menu zone, thus facilitating interaction speed and minimizing the false triggering rate. We built a dataset of 12 participants' natural gaze movements in typical VR applications. By analyzing their gaze movement patterns, we designed the menu UI personalization and optimized selection detection algorithm of GazeDock. We also examined users' gaze selection precision for targets on the peripheral menu and found that 4-8 menu items yield the highest throughput when considering both speed and accuracy. Finally, we validated the usability of GazeDock in a VR navigation game that contains both scene exploration and menu selection. Results showed that GazeDock achieved an average selection time of 471ms and a false triggering rate of 3.6%. And it received higher user preference ratings compared with dwell-based and pursuit-based techniques.
ISSN:2642-5254
DOI:10.1109/VR51125.2022.00105