Robust pervasive context-aware augmented reality (AR) has the potential to enable a range of applications that support users in reaching their personal and professional goals. In such applications, AR can be used to deliver richer, more immersive, and more timely just in time adaptive interventions (JITAI) than conventional mo-bile solutions, leading to more effective support of the user. This position paper defines a research agenda centered on improving AR applications' environmental, user, and social context awareness. Specifically, we argue for two key architectural approaches that will allow pushing AR context awareness to the next level: use of wearable and Internet of Things (IoT) devices as additional data streams that complement the data captured by the AR devices, and the development of edge computing-based mechanisms for enriching existing scene understanding and simultaneous localization and mapping (SLAM) algorithms. The paper outlines a collection of specific research directions in the development of such architectures and in the design of next-generation environmental, user, and social context awareness algorithms.
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Environmental, User, and Social Context-Aware Augmented Reality for Supporting Personal Development and Change
Robust pervasive context-aware augmented reality (AR) has the potential to enable a range of applications that support users in reaching their personal and professional goals. In such applications, AR can be used to deliver richer, more immersive, and more timely just in time adaptive interventions (JITAI) than conventional mobile solutions, leading to more effective support of the user. This position paper defines a research agenda centered on improving AR applications' environmental, user, and social context awareness. Specifically, we argue for two key architectural approaches that will allow pushing AR context awareness to the next level: use of wearable and Internet of Things (IoT) devices as additional data streams that complement the data captured by the AR devices, and the development of edge computing-based mechanisms for enriching existing scene understanding and simultaneous localization and mapping (SLAM) algorithms. The paper outlines a collection of specific research directions in the development of such architectures and in the design of next-generation environmental, user, and social context awareness algorithms.
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- NSF-PAR ID:
- 10315357
- Date Published:
- Journal Name:
- IEEE Workshop for Building the Foundations of the Metaverse
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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