提出智能AR的上下文框架与架构，实证研究用户适应行为。
Towards Intelligent Augmented Reality (iAR): A Taxonomy of Context, an Architecture for iAR, and an Empirical Study
- 构建可量化上下文的智能AR推理与自适应框架
- 实验发现上下文变化显著影响用户界面调整行为
- 适合研究人机交互与智能增强现实的开发者
近年来，增强现实（AR）研究凸显了上下文感知在提升界面效能与用户体验中的关键作用，这促使需要能够动态适应多种情境的智能增强现实（iAR）接口。本文（a）提出一个全面的上下文感知推理与自适应框架；（b）引入一种通过可量化的输入数据描述上下文的分类体系；（c）设计一个架构，将该框架与分类体系在iAR中具体实现。此外，我们开展了一项实证AR实验，观察用户在情境切换场景下的行为，记录用户表现、上下文信息及用户自定义的界面调整。我们（d）探索了上下文与用户适应之间的复杂关系，并讨论了该框架在识别这些模式中的意义。实验强调了上下文感知在iAR中的重要性，并提供了该特定场景的初步训练数据集。
原文摘要 · Abstract (English)
Recent advancements in Augmented Reality (AR) research have highlighted the critical role of context awareness in enhancing interface effectiveness and user experience. This underscores the need for intelligent AR (iAR) interfaces that dynamically adapt across various contexts to provide optimal experiences. In this paper, we (a) propose a comprehensive framework for context-aware inference and adaptation in iAR, (b) introduce a taxonomy that describes context through quantifiable input data, and (c) present an architecture that outlines the implementation of our proposed framework and taxonomy within iAR. Additionally, we present an empirical AR experiment to observe user behavior and record user performance, context, and user-specified adaptations to the AR interfaces within a context-switching scenario. We (d) explore the nuanced relationships between context and user adaptations in this scenario and discuss the significance of our framework in identifying these patterns. This experiment emphasizes the significance of context-awareness in iAR and provides a preliminary training dataset for this specific Scenario.
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