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8. Towards personalized virtual reality touring through cross-object user interfaces

  • Xiangdong Li , Yunzhan Zhou , Wenqian Chen , Preben Hansen , Weidong Geng and Lingyun Sun
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Personalized Human-Computer Interaction
This chapter is in the book Personalized Human-Computer Interaction

Abstract

Real-time adaptation is one of the most important problems that currently require a solution in the field of personalized human-computer interaction. For conventional desktop system interactions, user behaviors are acquired to develop models that support context-aware interactions. In virtual reality interactions, however, users operate tools in the physical world but view virtual objects in the virtual world. This dichotomy constrains the use of conventional behavioral models and presents difficulties to personalizing interactions in virtual environments. To address this problem, we propose the cross-object user interfaces (COUIs) for personalized virtual reality touring. COUIs consist of two components: a Deep Learning algorithm-based model using convolutional neural networks (CNNs) to predict the user’s visual attention from the past eye movement patterns to determine which virtual objects are likely to be viewed next, and delivery mechanisms that determine what should when and where be displayed on the user interface. In this chapter, we elaborate on the training and testing of the prediction model and evaluate the delivery mechanisms of COUIs through a cognitive walk-through approach. Furthermore, the implications for using COUIs to personalize interactions in virtual reality (and other environments such as augmented reality and mixed reality) are discussed.

Abstract

Real-time adaptation is one of the most important problems that currently require a solution in the field of personalized human-computer interaction. For conventional desktop system interactions, user behaviors are acquired to develop models that support context-aware interactions. In virtual reality interactions, however, users operate tools in the physical world but view virtual objects in the virtual world. This dichotomy constrains the use of conventional behavioral models and presents difficulties to personalizing interactions in virtual environments. To address this problem, we propose the cross-object user interfaces (COUIs) for personalized virtual reality touring. COUIs consist of two components: a Deep Learning algorithm-based model using convolutional neural networks (CNNs) to predict the user’s visual attention from the past eye movement patterns to determine which virtual objects are likely to be viewed next, and delivery mechanisms that determine what should when and where be displayed on the user interface. In this chapter, we elaborate on the training and testing of the prediction model and evaluate the delivery mechanisms of COUIs through a cognitive walk-through approach. Furthermore, the implications for using COUIs to personalize interactions in virtual reality (and other environments such as augmented reality and mixed reality) are discussed.

Chapters in this book

  1. Frontmatter I
  2. Introduction V
  3. Contents IX
  4. List of Contributing Authors XI
  5. Part I: Foundations of user modeling
  6. 1. Theory-grounded user modeling for personalized HCI 1
  7. 2. Opportunities and challenges of utilizing personality traits for personalization in HCI 31
  8. Part II: User input and feedback
  9. 3. Automated personalization of input methods and processes 67
  10. 4. How to use socio-emotional signals for adaptive training 103
  11. 5. Explanations and user control in recommender systems 133
  12. Part III: Personalization approaches
  13. 6. Tourist trip recommendations – foundations, state of the art, and challenges 159
  14. 7. Pictures as a tool for matching tourist preferences with destinations 183
  15. 8. Towards personalized virtual reality touring through cross-object user interfaces 201
  16. 9. User awareness in music recommender systems 223
  17. 10. Personalizing the user interface for people with disabilities 253
  18. 11. Adaptive workplace learning assistance 283
  19. Index 303
https://doi.org/10.1515/9783110552485-008

Chapters in this book

  1. Frontmatter I
  2. Introduction V
  3. Contents IX
  4. List of Contributing Authors XI
  5. Part I: Foundations of user modeling
  6. 1. Theory-grounded user modeling for personalized HCI 1
  7. 2. Opportunities and challenges of utilizing personality traits for personalization in HCI 31
  8. Part II: User input and feedback
  9. 3. Automated personalization of input methods and processes 67
  10. 4. How to use socio-emotional signals for adaptive training 103
  11. 5. Explanations and user control in recommender systems 133
  12. Part III: Personalization approaches
  13. 6. Tourist trip recommendations – foundations, state of the art, and challenges 159
  14. 7. Pictures as a tool for matching tourist preferences with destinations 183
  15. 8. Towards personalized virtual reality touring through cross-object user interfaces 201
  16. 9. User awareness in music recommender systems 223
  17. 10. Personalizing the user interface for people with disabilities 253
  18. 11. Adaptive workplace learning assistance 283
  19. Index 303
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