Towards Intelligent Augmented Reality (iAR): A Taxonomy of Context, an Architecture for iAR, and an Empirical Study

TL;DR

This paper proposes a comprehensive framework and taxonomy for context-aware intelligent augmented reality (iAR), introduces an architecture for implementing these concepts, and presents an empirical study demonstrating the importance of context-awareness in user adaptation and performance.

Contribution

It introduces a novel taxonomy and architecture for context-aware iAR, and provides empirical evidence of how context influences user behavior and interface adaptation.

Findings

Context-awareness significantly impacts user adaptation in iAR.

The empirical study reveals patterns of user behavior in context-switching scenarios.

The framework aids in identifying and leveraging context for improved iAR experiences.

Abstract

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.