The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been preselected and for which labeled training data are available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine. With this capability, we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individual's patterns and lifestyle. In this paper, we describe our activity mining and tracking approach, and validate our algorithms on data collected in physical smart environments.

中文翻译：

---

发现活动以在智能环境中识别和跟踪

智能家居中的机器学习和无处不在的传感技术为在家中独立生活困难的个人提供健康监测和帮助提供了前所未有的机会。为了监控智能家居居民的功能健康，我们需要设计能够识别和跟踪人们通常在日常生活中进行的活动的技术。虽然确实存在识别活动的方法，但这些方法适用于已预先选择的活动，并且有标记的训练数据可用。相比之下，我们引入了一种自动跟踪活动的方法，该方法可以识别在个人日常活动中自然发生的频繁活动。有了这个能力，然后，我们可以跟踪定期活动的发生，以监测功能健康并检测个人模式和生活方式的变化。在本文中，我们描述了我们的活动挖掘和跟踪方法，并根据在物理智能环境中收集的数据验证了我们的算法。