Effects of the physical environment on data visualization effectiveness in augmented reality

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Lytovchenko O. V., Мозіль Б. І., Думанський І. І. № 2 (90) 156-164 Image Image

This article analyzes the influence of real-world environments on the effectiveness of augmented reality (AR) data visualizations. AR content must compete with dynamic, cluttered backgrounds, creating significant cognitive challenges for users. This review systematizes recent scientific advancements addressing these core issues.

A primary focus is the shift from subjective self-reports to objective, real-time physiological measurements of cognitive load, particularly using eye-tracking metrics such as fixation duration and pupil dilation. These indicators are foundational for creating bio-adaptive interfaces that dynamically adjust visualizations to prevent user overload.

The article also analyzes strategies for mitigating visual clutter, highlighting effective label management techniques, such as grouping. It examines the influence of background characteristics on legibility, underscoring the trend toward AI-driven systems that automatically adjust visualization styles (e.g., contrast, outlines) to maintain readability and perceptual coherence.

Finally, the analysis covers innovative approaches to spatial content placement and ergonomics. It discusses moving beyond head-locked displays to paradigms like object-anchoring and utilizing unconventional surfaces (such as the floor, ceiling, or periphery) to free the user’s central vision. The ergonomic implications for mobile scenarios are explored, particularly the speed-accuracy trade-off associated with different content anchoring methods (e.g., head vs. hand).

In conclusion, this article discusses that the future of effective AR lies in holistic, context-aware systems that dynamically adapt to the environment, the user’s real-time cognitive state, individual traits, and task demands.

Keywords: data visualization, augmented reality, AR, cognitive load, visual clutter, situated analytics, adaptive interfaces, human–computer interaction.

doi: 10.32403/0554-4866-2025-2-90-156-164


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