imec-TELIN-IPI, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium.
Sensors (Basel). 2020 Apr 29;20(9):2513. doi: 10.3390/s20092513.
With the rapid development in sensing technology, data mining, and machine learning fields for human health monitoring, it became possible to enable monitoring of personal motion and vital signs in a manner that minimizes the disruption of an individual's daily routine and assist individuals with difficulties to live independently at home. A primary difficulty that researchers confront is acquiring an adequate amount of labeled data for model training and validation purposes. Therefore, activity discovery handles the problem that activity labels are not available using approaches based on sequence mining and clustering. In this paper, we introduce an unsupervised method for discovering activities from a network of motion detectors in a smart home setting. First, we present an intra-day clustering algorithm to find frequent sequential patterns within a day. As a second step, we present an inter-day clustering algorithm to find the common frequent patterns between days. Furthermore, we refine the patterns to have more compressed and defined cluster characterizations. Finally, we track the occurrences of various regular routines to monitor the functional health in an individual's patterns and lifestyle. We evaluate our methods on two public data sets captured in real-life settings from two apartments during seven-month and three-month periods.
随着人类健康监测在传感技术、数据挖掘和机器学习领域的快速发展，以最小化干扰个人日常生活的方式来监测个人运动和生命体征，并帮助有困难的个人独立在家生活成为可能。研究人员面临的一个主要困难是为模型训练和验证目的获取足够数量的标记数据。因此，活动发现使用基于序列挖掘和聚类的方法处理活动标签不可用的问题。在本文中，我们提出了一种在智能家居环境中从运动探测器网络中发现活动的无监督方法。首先，我们提出了一种日内聚类算法来找到一天内的频繁序列模式。作为第二步，我们提出了一种跨日聚类算法来找到日间的常见频繁模式。此外，我们还对模式进行了细化，使其具有更压缩和定义明确的聚类特征。最后，我们跟踪各种常规例程的发生情况，以监测个人模式和生活方式中的功能健康状况。我们在两个为期七个月和三个月的公寓中从两个公共数据集在真实环境中进行评估。
