ORCID: https://orcid.org/0000-0001-7127-8104; Weibel, Raphael P.
ORCID: https://orcid.org/0000-0002-8854-7507 and Feuerriegel, Stefan
ORCID: https://orcid.org/0000-0001-7856-8729
(2024):
Context-Adaptive Visual Cues for Safe Navigation in Augmented Reality Using Machine Learning.
In: International Journal of Human–Computer Interaction, Vol. 40, No. 3: pp. 761-781
[PDF, 3MB]
Abstract
Augmented reality (AR) using head-mounted displays (HMDs) is a powerful tool for user navigation. Existing approaches usually display navigational cues that are constantly visible (always-on). This limits real-world application, as visual cues can mask safety-critical objects. To address this challenge, we develop a context-adaptive system for safe navigation in AR using machine learning. Specifically, our system utilizes a neural network, trained to predict when to display visual cues during AR-based navigation. For this, we conducted two user studies. In User Study 1, we recorded training data from an AR HMD. In User Study 2, we compared our context-adaptive system to an always-on system. We find that our context-adaptive system enables task completion speeds on a par with the always-on system, promotes user autonomy, and facilitates safety through reduced visual noise. Overall, participants expressed their preference for our context-adaptive system in an industrial workplace setting.
Item Type: | Journal article |
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Keywords: | Artificial Intelligence, AI, Künstliche Intelligenz, KI |
Faculties: | Munich School of Management > Institute of Artificial Intelligence (AI) in Management |
Subjects: | 000 Computer science, information and general works > 000 Computer science, knowledge, and systems |
URN: | urn:nbn:de:bvb:19-epub-94958-1 |
ISSN: | 1044-7318 |
Annotation: | Published online: 22 Sep 2022 |
Language: | English |
Item ID: | 94958 |
Date Deposited: | 08. Mar 2023, 14:00 |
Last Modified: | 18. Jun 2024, 11:14 |