Pattern mining for routine behaviour discovery in pervasive healthcare environments
Pattern mining for routine behaviour discovery in pervasive healthcare environments
复制标题
普遍医疗保健环境中常规行为发现的模式挖掘
DOI：
10.1109/itab.2008.4570576
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Guang
中科院分区：
文献类型：
--
作者：
R. Ali;M. Elhelw;L. Atallah;B. Lo;Guang
通过对患者健康状况的持续和智能监测，普适传感将改变患者护理的未来。在实践中，检测不同时间分辨率的患者活动模式可能是一个复杂的过程，需要利用多层软件架构和处理大量数据。本文描述了一种可扩展的分布式软件架构，适用于管理由身体传感器网络生成的连续活动数据流。将一种新的模式挖掘算法应用于普适传感数据，以获得频繁活动模式随时间变化的简洁、变分辨率表示。这种频繁模式的识别使得观察到患者日常活动中存在的固有结构，从而分析常规行为及其偏差。
Pervasive sensing is set to transform the future of patient care by continuous and intelligent monitoring of patient well-being. In practice, the detection of patient activity patterns over different time resolutions can be a complicated procedure, entailing the utilisation of multi-tier software architectures and processing of large volumes of data. This paper describes a scalable, distributed software architecture that is suitable for managing continuous activity data streams generated from body sensor networks. A novel pattern mining algorithm is applied to pervasive sensing data to obtain a concise, variable-resolution representation of frequent activity patterns over time. The identification of such frequent patterns enables the observation of the inherent structure present in a patientpsilas daily activity for analyzing routine behaviour and its deviations.