research-article

Context-Aware Online Adaptation of Mixed Reality Interfaces

Authors:

David Lindlbauer,

Anna Maria Feit,

Otmar HilligesAuthors Info & Claims

UIST '19: Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology

Pages 147 - 160

https://doi.org/10.1145/3332165.3347945

Published: 17 October 2019 Publication History

Abstract

We present an optimization-based approach for Mixed Reality (MR) systems to automatically control when and where applications are shown, and how much information they display. Currently, content creators design applications, and users then manually adjust which applications are visible and how much information they show. This choice has to be adjusted every time users switch context, i.e., whenever they switch their task or environment. Since context switches happen many times a day, we believe that MR interfaces require automation to alleviate this problem. We propose a real-time approach to automate this process based on users' current cognitive load and knowledge about their task and environment. Our system adapts which applications are displayed, how much information they show, and where they are placed. We formulate this problem as a mix of rule-based decision making and combinatorial optimization which can be solved efficiently in real-time. We present a set of proof-of-concept applications showing that our approach is applicable in a wide range of scenarios. Finally, we show in a dual-task evaluation that our approach decreased secondary tasks interactions by 36%.

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Cited By

Yangfu RZhiqiang LSonghai Z(2024)Survey of visualization methods for multiscene visual cue information in immersive environmentsJournal of Image and Graphics10.11834/jig.22114729:1(1-21)Online publication date: 2024
https://doi.org/10.11834/jig.221147
Liao YMo GDudley JCheng CChan LKristensson POulasvirta A(2024)Practical approaches to group-level multi-objective Bayesian optimization in interaction technique designCollective Intelligence10.1177/263391372412413133:1Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1177/26339137241241313
Ilo CDiVerdi SBowman D(2024)Goldilocks Zoning: Evaluating a Gaze-Aware Approach to Task-Agnostic VR Notification PlacementProceedings of the 2024 ACM Symposium on Spatial User Interaction10.1145/3677386.3682087(1-12)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3677386.3682087
Show More Cited By

Index Terms

Context-Aware Online Adaptation of Mixed Reality Interfaces
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Mixed / augmented reality
      2. Virtual reality
    2. Interactive systems and tools
      1. User interface management systems

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cover image ACM Conferences

UIST '19: Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology

October 2019

1229 pages

ISBN:9781450368162

DOI:10.1145/3332165

General Chair:
François Guimbretière
Cornell University, USA
,
Program Chairs:
Michael Bernstein
Stanford University, USA
,
Katharina Reinecke
University of Washington, USA

Copyright © 2019 ACM.

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Published: 17 October 2019

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European Research Council
ETH Zurich Postdoctoral Fellowship Programme

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UIST '19

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UIST '19: The 32nd Annual ACM Symposium on User Interface Software and Technology

October 20 - 23, 2019

LA, New Orleans, USA

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Overall Acceptance Rate 842 of 3,967 submissions, 21%

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Cited By

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https://doi.org/10.11834/jig.221147
Liao YMo GDudley JCheng CChan LKristensson POulasvirta A(2024)Practical approaches to group-level multi-objective Bayesian optimization in interaction technique designCollective Intelligence10.1177/263391372412413133:1Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1177/26339137241241313
Ilo CDiVerdi SBowman D(2024)Goldilocks Zoning: Evaluating a Gaze-Aware Approach to Task-Agnostic VR Notification PlacementProceedings of the 2024 ACM Symposium on Spatial User Interaction10.1145/3677386.3682087(1-12)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3677386.3682087
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Lu FChen MHsu HDeshpande PWang CMacIntyre B(2024)Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement LearningExtended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613905.3651059(1-7)Online publication date: 11-May-2024
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