Activity Recognition for Smart Homes Using Dempster-Shafer Theory of Evidence Based on a Revised Lattice Structure
Activity Recognition for Smart Homes Using Dempster-Shafer Theory of Evidence Based on a Revised Lattice Structure
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使用基于修订的点阵结构的 Dempster-Shafer 证据理论进行智能家居活动识别
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
C. Nugent
中科院分区：
文献类型：
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作者：
Jing Liao;Y. Bi;C. Nugent
本文使用Dempster-Shafer的证据理论探讨了智能家庭环境中活动识别的改进。此方法除了管理基于传感器的读数中的不确定性外，还具有监视人类活动的能力。已经提出了一个三层晶格结构，可用于将传感器和传感器环境中得出的质量函数结合起来，随后可用于推断活动。从整个两个星期的共有209个记录的活动[9]中，考虑了85个厕所活动。这项工作的结果表明，这种方法能够在智能家庭环境中正确检测75个厕所活动，相当于分类精度为88.2％。
This paper explores an improvement to activity recognition within a Smart Home environment using the Dempster-Shafer theory of evidence. This approach has the ability to be used to monitor human activities in addition to managing uncertainty in sensor based readings. A three layer lattice structure has been proposed, which can be used to combine the mass functions derived from sensors along with sensor context and subsequently can be used to infer activities. From the total 209 recorded activities throughout a two week period [9], 85 toileting activities were considered. The results from this work demonstrated that this method was capable of detecting 75 of the toileting activities correctly within a Smart Home environment equating to a classification accuracy of 88.2%.