Pattern mining for routine behaviour discovery in pervasive healthcare environments

• Published in: 2008 International Conference on Information Technology and Applications in Biomedicine pp. 241 - 244
• Main Authors: Ali, R., ElHelw, M., Atallah, L., Lo, B., Guang-Zhong Yang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2008
• Subjects: activity recognition; behaviour profiling; Biomedical monitoring; Body sensor networks; Data visualization; frequent pattern mining; Medical services; Minimally invasive surgery; Patient monitoring; Pattern recognition; Senior citizens; Software architecture; Wearable sensors
• ISBN: 9781424422548; 142442254X
• ISSN: 2168-2194
• DOI: 10.1109/ITAB.2008.4570576

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.