Context-Aware Online Adaptation of Mixed Reality Interfaces

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%.

[1]  Antti Oulasvirta,et al.  MenuOptimizer: interactive optimization of menu systems , 2013, UIST.

[2]  David Lindlbauer,et al.  HeatSpace: Automatic Placement of Displays by Empirical Analysis of User Behavior , 2017, UIST.

[3]  Jodi Forlizzi,et al.  Psycho-physiological measures for assessing cognitive load , 2010, UbiComp.

[4]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[5]  Antti Oulasvirta,et al.  Aalto Interface Metrics (AIM): A Service and Codebase for Computational GUI Evaluation , 2018, UIST.

[6]  Steven K. Feiner,et al.  View management for virtual and augmented reality , 2001, UIST '01.

[7]  Krzysztof Z. Gajos,et al.  SUPPLE: automatically generating user interfaces , 2004, IUI '04.

[8]  E. Hess,et al.  Pupil Size in Relation to Mental Activity during Simple Problem-Solving , 1964, Science.

[9]  Christopher G. Atkeson,et al.  Predicting human interruptibility with sensors: a Wizard of Oz feasibility study , 2003, CHI '03.

[10]  Pushmeet Kohli,et al.  FLARE: Fast layout for augmented reality applications , 2014, 2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR).

[11]  Antti Oulasvirta,et al.  Ability-Based Optimization of Touchscreen Interactions , 2018, IEEE Pervasive Computing.

[12]  Martin Raubal,et al.  The Index of Pupillary Activity: Measuring Cognitive Load vis-à-vis Task Difficulty with Pupil Oscillation , 2018, CHI.

[13]  Albrecht Schmidt,et al.  A Model Relating Pupil Diameter to Mental Workload and Lighting Conditions , 2016, CHI.

[14]  Marc Alexa,et al.  OptiSpace: Automated Placement of Interactive 3D Projection Mapping Content , 2018, CHI.

[15]  John Sweller,et al.  Cognitive Load During Problem Solving: Effects on Learning , 1988, Cogn. Sci..

[16]  Antti Oulasvirta,et al.  User Interface Design with Combinatorial Optimization , 2017, Computer.

[17]  J. Golding Predicting individual differences in motion sickness susceptibility by questionnaire , 2006 .

[18]  Anna Maria Feit Assignment Problems for Optimizing Text Input , 2018 .

[19]  Antti Oulasvirta,et al.  Combinatorial Optimization for User Interface Design , 2018 .

[20]  Roman Rädle,et al.  Virtual Objects as Spatial Cues in Collaborative Mixed Reality Environments: How They Shape Communication Behavior and User Task Load , 2016, CHI.

[21]  Stephen DiVerdi,et al.  Level of detail interfaces , 2004, Third IEEE and ACM International Symposium on Mixed and Augmented Reality.

[22]  Jennifer Healey,et al.  Detecting stress during real-world driving tasks using physiological sensors , 2005, IEEE Transactions on Intelligent Transportation Systems.

[23]  Jean-Pierre Jessel,et al.  Adaptive augmented reality: plasticity of augmentations , 2014, VRIC.

[24]  Eyal Ofek,et al.  SnapToReality: Aligning Augmented Reality to the Real World , 2016, CHI.

[25]  Dieter Schmalstieg,et al.  Adaptive information density for augmented reality displays , 2016, 2016 IEEE Virtual Reality (VR).

[26]  Jens Grubert,et al.  Extended investigations of user-related issues in mobile industrial AR , 2010, 2010 IEEE International Symposium on Mixed and Augmented Reality.

[27]  Steven K. Feiner,et al.  Information filtering for mobile augmented reality , 2000, Proceedings IEEE and ACM International Symposium on Augmented Reality (ISAR 2000).

[28]  Sandra P. Marshall,et al.  Measuring cognitive workload across different eye tracking hardware platforms , 2012, ETRA.

[29]  J. B. Brooke,et al.  SUS: A 'Quick and Dirty' Usability Scale , 1996 .

[30]  Yang Wang,et al.  Detecting Users’ Cognitive Load by Galvanic Skin Response with Affective Interference , 2017, ACM Trans. Interact. Intell. Syst..

[31]  Holger Regenbrecht,et al.  Towards Pervasive Augmented Reality: Context-Awareness in Augmented Reality , 2017, IEEE Transactions on Visualization and Computer Graphics.

[32]  Daniel Afergan,et al.  Learn Piano with BACh: An Adaptive Learning Interface that Adjusts Task Difficulty Based on Brain State , 2016, CHI.

[33]  S. P. Marshall,et al.  The Index of Cognitive Activity: measuring cognitive workload , 2002, Proceedings of the IEEE 7th Conference on Human Factors and Power Plants.

[34]  Roman Rädle,et al.  AdaM: Adapting Multi-User Interfaces for Collaborative Environments in Real-Time , 2018, CHI.

[35]  Jean Vanderdonckt,et al.  A computational framework for context-aware adaptation of user interfaces , 2013, IEEE 7th International Conference on Research Challenges in Information Science (RCIS).

[36]  Paolo Toth,et al.  Knapsack Problems: Algorithms and Computer Implementations , 1990 .

[37]  Ronald Azuma,et al.  Evaluating label placement for augmented reality view management , 2003, The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Proceedings..

[38]  Dieter Schmalstieg,et al.  Image-driven view management for augmented reality browsers , 2012, 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR).

[39]  Antti Oulasvirta,et al.  Improvements to keyboard optimization with integer programming , 2014, UIST.

[40]  P. Dourish,et al.  Context-Aware Computing , 2001 .

[41]  Anind K. Dey,et al.  Context-Aware Computing , 2010, Ubicomp 2010.

[42]  Henry Lieberman,et al.  Out of context: Computer systems that adapt to, and learn from, context , 2000, IBM Syst. J..

[43]  Tom Drummond,et al.  Real-Time Video Annotations for Augmented Reality , 2005, ISVC.

[44]  J.K. Aggarwal,et al.  Human activity analysis , 2011, ACM Comput. Surv..

[45]  Frank Biocca,et al.  Comparative effectiveness of augmented reality in object assembly , 2003, CHI '03.

[46]  Claus B. Madsen,et al.  Temporal Coherence Strategies for Augmented Reality Labeling , 2016, IEEE Transactions on Visualization and Computer Graphics.

[47]  Brian P. Bailey,et al.  Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management , 2008, TCHI.