论文信息 - Pattern mining for routine behaviour discovery in pervasive healthcare environments

Pattern mining for routine behaviour discovery in pervasive healthcare environments

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.

B. Lo | R. Ali | M. ElHelw | L. Atallah | Guang-Zhong Yang

[1] Paul J. Perrone,et al. J2EE Developer's Handbook , 2003 .

[2] Philip S. Yu,et al. A Survey of Synopsis Construction in Data Streams , 2007, Data Streams - Models and Algorithms.

[3] Jian Pei,et al. CLOSET+: searching for the best strategies for mining frequent closed itemsets , 2003, KDD '03.

[4] Peter Sommerlad,et al. Pattern-Oriented Software Architecture Volume 1: A System of Patterns , 1996 .

[5] Yossi Matias,et al. DIMACS Series in Discrete Mathematicsand Theoretical Computer Science Synopsis Data Structures for Massive Data , 2007 .

[6] Umakishore Ramachandran,et al. MediaBroker: an architecture for pervasive computing , 2004, Second IEEE Annual Conference on Pervasive Computing and Communications, 2004. Proceedings of the.

[7] Guang-Zhong Yang,et al. Behaviour Profiling with Ambient and Wearable Sensing , 2007, BSN.

[8] Guang-Zhong Yang,et al. Real-Time Pervasive Monitoring for Postoperative Care , 2007, BSN.

[9] A. Darzi,et al. A Pervasive Body Sensor Network for Measuring Postoperative Recovery at Home , 2007, Surgical innovation.

[10] Anthony K. H. Tung,et al. Carpenter: finding closed patterns in long biological datasets , 2003, KDD '03.

[11] Philip S. Yu,et al. Mining Frequent Patterns in Data Streams at Multiple Time Granularities , 2002 .

[12] Salvatore J. Stolfo,et al. Real time data mining-based intrusion detection , 2001, Proceedings DARPA Information Survivability Conference and Exposition II. DISCEX'01.