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Summary: Abstract-- 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 the 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.
Keywords body sensor networks, frequent pattern mining,
activity recognition, behaviour profiling
I. INTRODUCTION
HE recent emergence of pervasive healthcare has
enabled the monitoring of chronically ill patients or
elderly in their own home environments [1]. This can result
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