Investigating the Impact of Real-World Environments on the Perception of 2D Visualizations in Augmented Reality

Document Title

Investigating the Impact of Real-World Environments on the Perception of 2D Visualizations in Augmented Reality

Document Information

• Topic Area: Application of augmented reality (AR) technology in data visualization and industrial scenarios, and research on visual perception
• Keywords: user study, augmented reality, visual perception, immersive analytics, in-situ visualization, AR visualization, industrial scenarios, Industry 4.0

Research Background and Issues

• Identified Problems or Challenges: As augmented reality technology gradually integrates into daily life and industrial scenarios, research on the perception and visual effects of information presentation in AR environments remains insufficient. Specifically, how to optimize data visualization in complex real-world environments is still unclear.
• Importance: AR technology has the potential to improve work efficiency and data comprehension in production environments, but the complexity of background environments may affect users' understanding of visualization information and interaction efficiency.
• Research Motivation and Related Work: Using Industry 4.0 production environments as a case study, this research investigates how background visual complexity and secondary tasks influence users' perception and performance in AR visualization. It also integrates findings from existing studies on text styles, graphical elements, and background effects.

Solution

• Proposed Methods and Solutions: The authors designed and conducted two user studies to explore the impact of background complexity on AR visualization and the effect of introducing secondary observation tasks on user perception.
• Innovations: (1) Conducting user studies in real industrial environments rather than laboratories; (2) Systematically combining background characteristics and visual task complexity; (3) Introducing secondary tasks to simulate attention allocation issues in real-world scenarios.
• Implementation Steps and Key Techniques:
  • Constructing industrial modules and dynamic elements (e.g., moving water flow, light reflections) using real backgrounds.
  • Utilizing Microsoft HoloLens v1 for display and interaction.
  • Quantifying background visual complexity using the Feature Congestion (FC) method.
  • Designing two experiments:
    • First experiment: Users completed data analysis tasks under varying background and visualization complexity.
    • Second experiment: Introducing secondary tasks (identifying changes on background displays) to force users to switch attention between the primary visual task and the background.

Research Findings

• Specific Results:
  • First Study: Background visual complexity had minimal objective impact on user task performance, but users subjectively perceived complex backgrounds as more distracting. Task completion time was not significantly affected, though background features significantly increased users' perceived task difficulty.
  • Second Study: Secondary tasks increased users' attention to the background but had no significant impact on primary task performance. Complex backgrounds did extend task completion time but did not reduce answer accuracy.
  • Users were able to perform tasks well in complex real-world backgrounds, but background characteristics (e.g., dynamic elements, reflective light) significantly influenced subjective experience.
• Advantages Over Existing Solutions:
  • Compared to studies conducted solely in laboratories, experiments in real environments revealed broader impacts of visual complexity on application scenarios.
  • Provided systematic research data based on task complexity and background visual variables, supporting future studies.
• Experimental or Evaluation Results:
  • Task performance was not significantly affected by background visual complexity, but users' subjective perception indicated greater distraction in complex backgrounds.
  • The introduced secondary task (recognition of dynamic numbers in the background) successfully increased users' attention to the background without significantly impacting primary task outcomes.
• Limitations and Future Directions:
  • Limitations: The study used a single visualization type (line charts), did not consider dynamic data, and background configurations may limit generalization to other application scenarios. The device (HoloLens v1) was constrained by a small field of view and comfort issues. The participant sample size was small and focused on university students.
  • Future Directions:
    • Investigate cognitive impacts of other visualization types (e.g., bar charts or scatter plots).
    • Explore the combined effects of multitasking and dynamic data on user perception.
    • Conduct deeper studies on subjective perception and performance across diverse user groups (e.g., broader age ranges).
    • Improve methods for quantifying background complexity, incorporating dynamic changes and scene characteristics.

Conclusion

Through two user studies, this paper analyzed the effects of real-world background environments and secondary tasks on the perception of AR data visualization. The research demonstrated that while background complexity had limited impact on task performance, it significantly influenced subjective perception. These findings provide valuable guidance for designing practical augmented reality tools. The study also emphasizes the importance of user experience and visual design in optimizing AR technology for productivity and human-computer interaction in industrial scenarios.