Sprint Gina, Cook Diane, Fritz Roschelle, Schmitter-Edgecombe Maureen
Washington State University, Pullman, WA 99164-2752.
Computer (Long Beach Calif). 2016 Nov;49(11):29-37. doi: 10.1109/mc.2016.338. Epub 2016 Nov 11.
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.
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。
