Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Sensors (Basel). 2017 Apr 26;17(5):952. doi: 10.3390/s17050952.
The understanding of various health-oriented vital sign data generated from body sensor networks (BSNs) and discovery of the associations between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where occupants' health status is continuously monitored remotely, it is essential to provide the required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach for mining the periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce productive-associated periodic-frequent patterns as the set of correlated periodic-frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients to raise the quality of diagnosis as well as improve treatment and smart care, especially for elderly people in smart homes. We develop an efficient algorithm named PPFP-growth (Productive Periodic-Frequent Pattern-growth) to discover all productive-associated periodic frequent patterns using these measures. PPFP-growth is efficient and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-growth algorithm, which can filter a huge number of periodic patterns to reveal only the correlated ones.
从身体传感器网络 (BSN) 生成的各种面向健康的生命体征数据的理解，以及发现生成参数之间的关联，是一项重要任务，可能有助于并促进医疗保健中的重要决策。例如，在一个智能家居场景中，居住者的健康状况被远程持续监测，当在他们的生命体征数据中检测到异常或危急情况时，提供所需的帮助至关重要。在本文中，我们提出了一种从 BSN 数据中挖掘周期性模式的有效方法。此外，我们对生成的模式进行相关性测试，并引入相关周期性频繁模式作为相关周期性频繁项的集合。这些措施的结合具有使医疗保健提供者和患者提高诊断质量、改善治疗和智能护理的优势，特别是对于智能家居中的老年人。我们开发了一种名为 PPFP-growth（生产性周期性频繁模式-增长）的有效算法，使用这些措施来发现所有生产性相关的周期性频繁模式。PPFP-growth 算法效率高，并且生产性度量去除了不相关的周期性项。对合成数据集和真实数据集的实验评估表明，所提出的 PPFP-growth 算法的效率很高，可以过滤大量的周期性模式，只显示相关的模式。
