Pattern mining for routine behaviour discovery in pervasive healthcare environments

被引：0

作者：

Ali, Raza ^{[1
]}

ElHelw, Mohamed ^{[1
]}

Atallah, Louis ^{[1
]}

Lo, Benny ^{[1
]}

Yang, Guang-Zhong ^{[1
]}

机构：

[1] Univ London Imperial Coll Sci Technol & Med, Dept Comp, London SW7 2AZ, England

来源：

2008 INTERNATIONAL SPECIAL TOPIC CONFERENCE ON INFORMATION TECHNOLOGY AND APPLICATIONS IN BIOMEDICINE, VOLS 1 AND 2 | 2008年

关键词：

body sensor networks; frequent pattern mining; activity recognition; behaviour profiling;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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 patient's daily activity for analyzing routine behaviour and its deviations.

引用

页码：216 / 219

页数：4

共 13 条

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