Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve, which represents an abstraction of an individual’s normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics.

普适计算提供了前所未有的机会，可以不引人注目地监视行为并使用收集的大量数据对基于活动的行为模式进行分析。在本文中，我们引入了活动曲线的概念，它代表了基于自动识别活动的个人正常日常生活的抽象。我们提出了通过比较活动曲线来检测行为习惯变化的方法，并利用这些变化来分析认知或身体健康变化的可能性。我们使用从 18 个老年居民智能家居中收集的纵向智能家居传感器数据集来展示我们的模型并评估我们的变化检测方法。最后，我们演示了如何使用基于大数据的普遍分析（例如基于活动曲线的变化检测）来执行功能健康评估。我们的评估表明，行为和健康变化之间确实存在相关性，并且可以使用智能家居、机器学习和基于大数据的普遍分析来自动检测这些变化。