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AUIT – the Adaptive User Interfaces Toolkit for Designing XR Applications

Publikation: Bidrag til bog/antologi/rapport/proceeding › Konferencebidrag i proceedings › Forskning › peer review

DOI

Adaptive user interfaces can improve experiences in Extended Reality (XR) applications by adapting interface elements according to the user’s context. Although extensive work explores different adaptation policies, XR creators often struggle with their implementation, which involves laborious manual scripting. The few available tools are underdeveloped for realistic XR settings where it is often necessary to consider conflicting aspects that affect an adaptation. We fill this gap by presenting AUIT, a toolkit that facilitates the design of optimization-based adaptation policies. AUIT allows creators to flexibly combine policies that address common objectives in XR applications, such as element reachability, visibility, and consistency. Instead of using rules or scripts, specifying adaptation policies via adaptation objectives simplifies the design process and enables creative exploration of adaptations. After creators decide which adaptation objectives to use, a multi-objective solver finds appropriate adaptations in real-time. A study showed that AUIT allowed creators of XR applications to quickly and easily create high-quality adaptations.
OriginalsprogEngelsk
TitelUIST '22 : Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology
ForlagAssociation for Computing Machinery
Udgivelsesårokt. 2022
Artikelnummer48
ISBN (trykt)9781450393201
DOI
StatusUdgivet - okt. 2022
BegivenhedUIST'22: The 35th Annual ACM Symposium on User Interface Software and Technology - OR, Bend, USA
Varighed: 29 okt. 2022 → 2 nov. 2022

Konference

KonferenceUIST'22: The 35th Annual ACM Symposium on User Interface Software and Technology
LandUSA
ByOR, Bend
Periode29/10/2022 → 02/11/2022

Bibliografisk note

Funding Information:
This work was supported by the Innovation Fund Denmark (IFD grant no. 6151-00006B) as part of the Manufacturing Academy of Denmark (MADE) Digital project. Antti Oulasvirta was supported by the Finnish Center for Artifcial Intelligence (FCAI), and Academy of Finland grants ‘Human Automata’ and ‘BAD’. Special thanks to Aïna Linn Georges for the help with revisions and the anonymous reviewers for constructive feedback that helped improve the paper.

Publisher Copyright:
© 2022 Owner/Author.

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