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利用智能家居检测和分析健康事件。

Using Smart Homes to Detect and Analyze Health Events.

作者信息

Sprint Gina, Cook Diane, Fritz Roschelle, Schmitter-Edgecombe Maureen

机构信息

Washington State University, Pullman, WA 99164-2752.

出版信息

Computer (Long Beach Calif). 2016 Nov;49(11):29-37. doi: 10.1109/mc.2016.338. Epub 2016 Nov 11.

Abstract

Instrumented smart homes offer an unprecedented opportunity to unobtrusively monitor human behavior in natural environments. Additionally, they can be used to determine whether relationships exist between behavior and health changes. Here we introduce an approach to behavior change detection (BCD) that can be used to identify behavior changes that accompany health events. BCD detects changes between time periods, determines significance of the detected changes, and analyzes the nature of the changes. In the case of smart homes, sensor data is collected and labeled using activity recognition and BCD is applied to analyze behavior changes by quantifying and analyzing changes in the activity timings and durations. We demonstrate our approach using three case studies for older adults living in smart homes who experienced major health events. Our evaluation indicates that behavior changes consistent with the medical literature do occur in these cases and that the changes can be automatically detected using BCD. The proposed smart home, activity recognition, and change detection algorithms are useful data mining techniques for understanding the behavioral effects of health conditions.

摘要

配备仪器的智能家居提供了一个前所未有的机会,可以在自然环境中对人类行为进行不引人注意的监测。此外,它们还可用于确定行为与健康变化之间是否存在关联。在此,我们介绍一种行为变化检测(BCD)方法,该方法可用于识别伴随健康事件发生的行为变化。BCD可检测不同时间段之间的变化,确定所检测变化的显著性,并分析变化的性质。对于智能家居而言,会收集传感器数据并使用活动识别进行标注,然后应用BCD通过量化和分析活动时间和时长的变化来分析行为变化。我们通过三个针对居住在智能家居中的老年人经历重大健康事件的案例研究来展示我们的方法。我们的评估表明,在这些案例中确实出现了与医学文献一致的行为变化,并且这些变化可以使用BCD自动检测到。所提出的智能家居、活动识别和变化检测算法是用于理解健康状况行为影响的有用数据挖掘技术。

相似文献

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Using Smart Homes to Detect and Analyze Health Events.利用智能家居检测和分析健康事件。
Computer (Long Beach Calif). 2016 Nov;49(11):29-37. doi: 10.1109/mc.2016.338. Epub 2016 Nov 11.
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本文引用的文献

1
Modeling Patterns of Activities using Activity Curves.使用活动曲线对活动模式进行建模。
Pervasive Mob Comput. 2016 Jun;28:51-68. doi: 10.1016/j.pmcj.2015.09.007.
2
Automated Cognitive Health Assessment From Smart Home-Based Behavior Data.基于智能家居行为数据的自动化认知健康评估
IEEE J Biomed Health Inform. 2016 Jul;20(4):1188-94. doi: 10.1109/JBHI.2015.2445754. Epub 2015 Aug 17.
3
Activity Recognition on Streaming Sensor Data.流传感器数据的活动识别
Pervasive Mob Comput. 2014 Feb 1;10(Pt B):138-154. doi: 10.1016/j.pmcj.2012.07.003.
4
CASAS: A Smart Home in a Box.卡萨斯:一个集成式智能家居。
Computer (Long Beach Calif). 2013 Jul;46(7). doi: 10.1109/MC.2012.328.
5
Procedures for numerical analysis of circadian rhythms.昼夜节律的数值分析程序。
Biol Rhythm Res. 2007;38(4):275-325. doi: 10.1080/09291010600903692.
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