[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126814-en":3,"doc-seo-126814-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126814,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",6,"Technology","Context-Adaptive Visual Cues for Safe Navigation in Augmented Reality Using Machine Learning","Augmented reality (AR) with head-mounted displays (HMDs) supports navigation by overlaying visual guidance toward areas of interest. Many existing designs use always-on cues, which can obscure or distract from safety-critical objects and reduce situational awareness in demanding settings such as industrial work. To address this conflict, the paper introduces a machine-learning approach that predicts when navigational cues should be shown during AR navigation, learning from recorded HMD data and validating against an always-on baseline through two user studies.","International Journal of Human–Computer Interaction  \nISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/hihc20)[www.tandfonline.com/journals/hihc20](homepage: www.tandfonline.com/journals/hihc20)  \nContext-Adaptive Visual Cues for Safe Navigation in Augmented Reality Using Machine Learning  \nArne Seeliger, Raphael P. Weibel & Stefan Feuerriegel  \nTo cite this article: Arne Seeliger, Raphael P. Weibel & Stefan Feuerriegel (2024) Context-Adaptive Visual Cues for Safe Navigation in Augmented Reality Using Machine Learning, International Journal of Human–Computer Interaction, 40:3, 761-781, DOI:  \n10. 1080/10447318 .2022.2122114  \nTo link to this article: [https://doi.org/10.1080/10447318.2022.21221](https://doi.org/10.1080/10447318.2022.21221)14  \n© 2022 The Author(s) . Published with license by Taylor & Francis Group, LLC.  \n\n|  Published online: 22 Sep 2022. |  |\n| --- | --- |\n|  Submit your article to this journal  |  |\n|  Article views: 2588 |  |\n|  | View related articles  |\n|  | View Crossmark data |\n|  Citing articles: 6 View citing articles  |  |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=hihc20](https://www.tandfonline.com/action/journalInformation?journalCode=hihc20)  \nINTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION 2024, VOL. 40, NO. 3, 761–781  \n[https://doi.org/10.1080/10447318.2022.2122114](https://doi.org/10.1080/10447318.2022.2122114)  \nContext-Adaptive Visual Cues for Safe Navigation in Augmented Reality Using Machine Learning  \nArne Seeliger 􀀁 , Raphael P. Weibel 􀀁 , and Stefan Feuerriegel   \nETH Zurich, Zurich, Switzerland  \nABSTRACT  \nAugmented 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 contextadaptive system in an industrial workplace setting.  \n1. Introduction  \nBy combining the physical and the virtual world, augmented reality (AR) technologies can facilitate effective user navigation. When using head-mounted displays (HMDs), navigation is often achieved by displaying visual cues that guide users toward areas of interest (AOIs) . In these cases, the navigational guidance provided is usually identical for all users (Wang et al., 2016) . More specifically, visual cues are typically always-on, meaning always visible in the user’s field of view (FOV) . This is problematic, as it has been shown that AR-based navigation cues can overlap with real objects, thereby leading to distractions and obscured obstacles (Arntz et al., 2020; Krupenia & Sanderson, 2006; Liu et al., 2009) . For example, in industrial settings, users need to pay close attention to potentially hazardous elements like heavy machinery or dangerous materials. In these settings, Kim et al. (2016) found that AR HMDs may increase distraction and reduce situational awareness. Therefore, in many situations, an inherent trade-off between effective user navigation and safety arises. This conflict of objectives poses a challenge for the design of AR-based navigation systems.  \nFor AR HMDs, one potential solution to the trade-off between effective user navigation and ","cbCaioWjq4bMlrHH","https://ap.wps.com/l/cbCaioWjq4bMlrHH","pdf",3970250,1,22,"English","en",105,"# Abstract\n# Introduction\n## Trade-off in AR navigation: guidance vs. safety\n## Limitations of explicit user input\n## Using implicit user and environmental context","[{\"question\":\"Why are always-on AR navigation cues a safety problem?\",\"answer\":\"Always-on cues remain visible in the user’s field of view and can overlap with real objects, causing distractions and obscuring obstacles, which can reduce situational awareness.\"},{\"question\":\"What solution does the paper propose for safe navigation in AR?\",\"answer\":\"It proposes a context-adaptive system that uses a neural network to predict when to display visual cues during AR-based navigation.\"},{\"question\":\"How was the proposed system evaluated?\",\"answer\":\"The authors ran two user studies: one to record training data from an AR HMD, and another to compare the context-adaptive system with an always-on cue approach.\"}]","Context-Adaptive Visual Cues for Safe Navigation in Augmented Reality Using Machine Learning | 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