分层广义上下文推理或上下文感知智能家居

Chao-Lin Wu, Mao-Yung Weng, Ching-Hu Lu, L. Fu
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引用次数: 7

摘要

人类活动是环境感知智能家居的关键信息之一,因为了解正在进行的活动对于提供适当的服务非常重要。之前的大部分工作主要集中在识别个人活动上,因此需要很高的成本来跟踪人,并且当有多个用户时性能不佳,这在真实的家庭环境中很常见。因此,我们提出了分层广义上下文推理来推断多用户上下文。该方法通过将多用户上下文视为由聚合实体引起的广义上下文,对这些具有不同信息粒度的多用户上下文进行泛化,然后对这些广义上下文进行动态推断和聚合。基于广义上下文的推理结果,上下文感知智能家居可以尽可能多地提供相应的服务。实验结果证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hierarchical generalized context inference or context-aware smart homes
Human activity is among the critical information for a context-aware smart home since knowing what activities are undertaken is important for providing appropriate services. Most of the prior works primarily focus on recognizing individual activity, thus requiring high cost to track people and performs not well when there are multiple users, which is common in a real home environment. Therefore, we propose hierarchical generalized context inference to infer multi-user contexts. By treating a multi-user context as a generalized context caused by an aggregated entity, our approach generalizes these multi-user contexts with different information granularity, and then dynamically infers and aggregates these generalized contexts. Based on the inference results of generalized contexts, a context-aware smart home can provide appropriate services as much as possible. Our experimental results demonstrate the effectiveness of the proposed approach.
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