用于共享教育空间的上下文感知无标记增强现实

T. Scargill
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引用次数: 0

摘要

为了使无标记增强现实(AR)在教育环境中充分发挥其潜力,它必须能够适应影响学习结果的各种可能的环境、设备和用户认知状态。共享的教育空间,如教室、艺术画廊、博物馆、教学医院和野生动物中心,在专门的AR应用程序、现有的基础设施和大量的AR会话收集数据方面提供了诱人的机会。在这项工作中,我们通过使用“本地专家”使具有挑战性的上下文感知AR概念成为可能,该专家学习AR算法的最佳配置,并为特定的教育空间和用例实现虚拟内容。为了计算来自多个AR设备的见解,能够及时响应快速变化的用户认知状态,并确保敏感用户数据的安全性,我们提出了一种边缘计算架构,其中与我们的本地专家相关的存储和计算在与移动AR设备相同的局域网服务器上执行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Context-Aware Markerless Augmented Reality for Shared Educational Spaces
In order for markerless augmented reality (AR) to reach its full potential in educational settings it must be able to adapt to the wide range of possible environments, devices and user cognitive states that affect learning outcomes. Shared educational spaces, such as classrooms, art galleries, museums, teaching hospitals and wildlife centers present enticing opportunities in terms of specialized AR applications, existing infrastructure to leverage, and large numbers of AR sessions to gather data on. In this work we make feasible the challenging concept of context-aware AR through the use of a ‘local expert’, which learns the optimal configuration of AR algorithms and virtual content for the specific educational space and use case for which it is implemented. To compute insights from multiple AR devices, enable timely responses to fast-changing user cognitive states, and ensure the security of sensitive user data, we propose an edge-computing architecture, in which storage and computation related to our local expert is performed on a server on the same local area network as the mobile AR devices.
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