Pattern mining for routine behaviour discovery in pervasive healthcare environments - Ali, R.; ElHelw, M.; Atallah, L.; Lo, B.; Guang-Zhong Yang (2008 International Conference on Information Technology and Applications in Biomedicine, 2008)
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.