Detecting abnormal events on binary sensors in smart home environments
Detecting abnormal events on binary sensors in smart home environments
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DOI：
10.1016/j.pmcj.2016.06.012
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发表时间：
2016-12-01
影响因子：
4.3
通讯作者：
Dobson, Simon
中科院分区：
文献类型：
--
作者：
Ye, Juan;Stevenson, Graeme;Dobson, Simon
随着人口老龄化的加剧，智能家居技术已被证明是实现技术驱动的医疗保健服务的一个有前途的范例。智能家居技术由先进的传感、计算和通信技术组成，为跟踪老年人的行为和活动提供了前所未有的机会，并提供了环境感知服务，使老年人能够在自己的家中保持活跃和独立。然而，在开发的原型中的实验表明，异常传感器事件妨碍了关键（和潜在的生命威胁）情况的正确识别，并且当应用于共享生活空间的多个人时，现有的学习、估计和基于时间的情况识别方法是不准确和不灵活的。我们提出了一种新的技术，称为清洁，集成了语义的传感器读数与统计离群值检测。我们对不同环境中的四个真实数据集（包括多个居民的数据集）进行了评估。实验结果表明，CLEAN算法能够有效地检测传感器异常，提高了活动识别的准确率。(C)2016爱思唯尔B.V.保留所有权利。
With a rising ageing population, smart home technologies have been demonstrated as a promising paradigm to enable technology-driven healthcare delivery. Smart home technologies, composed of advanced sensing, computing, and communication technologies, offer an unprecedented opportunity to keep track of behaviours and activities of the elderly and provide context-aware services that enable the elderly to remain active and independent in their own homes. However, experiments in developed prototypes demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time based approaches to situation recognition are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a novel technique, called CLEAN, that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets across different environments including the datasets with multiple residents. The results have shown that CLEAN can successfully detect sensor anomaly and improve activity recognition accuracies. (C) 2016 Elsevier B.V. All rights reserved.