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Gaze-driven placement of items for proactive visual exploration.

Shigeo Takahashi1, Akane Uchita1, Kazuho Watanabe2

  • 1Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, 965-8580 Japan.

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This study introduces optimized item layouts for digital signage, using eye-tracking to dynamically arrange content. This improves visual search by adapting displays based on user interest and context.

Keywords:
Gaze-driven interactionOptimizationSearch contextUser evaluation

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Area of Science:

  • Human-Computer Interaction
  • Information Visualization
  • Digital Signage Systems

Background:

  • Digital signage advances enable dynamic item display but lack sophisticated layout schemes.
  • Effective item selection within groups on digital displays remains a research challenge.
  • Context-aware layouts are crucial for enhancing user experience in visual search tasks.

Purpose of the Study:

  • To explore sophisticated item layout schemes for digital signage that respect search context.
  • To develop methods for both static and dynamic item placement optimizing visual exploration.
  • To infer user preferences and guide dynamic content updates based on eye gaze patterns.

Main Methods:

  • Formulated static item placement as an optimization problem with aesthetic constraints.
  • Developed dynamic item placement driven by eye-tracking data to infer attractive item progression.
  • Utilized matrix factorization on annotated text to extract topics and establish context maps for item replacement.

Main Results:

  • Proposed a context-aware planar layout (context map) for dynamic item association and replacement.
  • Demonstrated that eye gaze analysis can guide animated layouts towards desired items.
  • User studies validated the design criteria for both static and dynamic item placement strategies.

Conclusions:

  • The proposed approach offers a framework for sophisticated, context-aware item layouts in digital signage.
  • Dynamic, eye-gaze-driven layouts enhance proactive visual exploration and item selection.
  • Further research can explore advanced association rules and real-time adaptation for improved user engagement.