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 in lower costs, reduced strain on healthcare, and more convenience for patients and their social carers [2, 3]. Pervasive sensing is also valuable for measuring post- operative recovery, which is particularly important for minimally invasive surgery as early signs of post-operative complication can only be captured in a home environment because patients are usually discharged much more quickly compared to that of conventional surgery [3]. Current designs of pervasive sensing are also working towards integrating ambient and wearable sensing for continuous monitoring of key physiological and activity indices of patients. Alteration of daily activities and deviations from normal behaviour can manifest the onset or progression of disease. Pervasive healthcare deployments entail a diverse set of software needs including interoperability with legacy sensors, web services requirements, varying levels of security expectations and visualization for different system users. In addition, the need to continuously gather and process large volumes of sensor data, coupled with repeated querying of the database, would introduce significant Manuscript received 29 Feb 2008 R. Ali, M. ElHelw, L. Atallah, B. Lo and G.Z. Yang are with the Department of Computing, Imperial College, London. E-mail:{smrali, me, latallah,benlo,gzy}@doc.ic.ac.uk. computational and storage loadings. This problem is exacerbated when a large number of users need to be catered for at the same time. As a result, algorithmic and system- and functional-level complexities comprise significant challenges to pervasive healthcare system developments. In this case, rigid software architectures designed to be application- specific cannot cope with the diverse and evolving requirements of this rapidly evolving field of research and development. Recently, a number of light-weight software methodologies suitable for scalable data processing, transmission and storage have been introduced [4,5]. Synopsis structures [4,6], for example, can act as substantially smaller visualization and querying surrogates for the actual data for a specific set of queries. Techniques such as wavelets, histograms, sketches and sub-sampling can also reduce the resource utilisation of data dramatically, thus freeing up main memory, lowering access rates at the database, and improving responsiveness for web clients. For these reasons, there has been an increasing interest in efficient methods for synopses for data streams [4]. Behaviour profiling is an important area in pervasive sensing [7]. The onset or complication of a life threatening episode may be marked by changes in behaviour and activity patterns. For example, in the elderly population, prostatism, degenerative joint disease, bursitis, and gastro-esophageal reflux are common causes of frequent awakening episodes and disturbed sleep, along with congestive heart failure, coronary artery disease, and chronic obstructive pulmonary disease. Sophisticated algorithms can recognize an increasing range of user’s activities from wearable and ambient sensor data [3]. While there is significant variability in human activity, there is typically a repeating, structure over the long term. Finding this routine pattern from activity information is an important step towards the understanding of the general behaviour of the patient. To this end, frequent pattern mining [8,9,10,11] constitutes a promising technique for the discovery of rich routine information and has been successfully applied to a number of applications [9,11]. This paper proposes a framework for routine pattern discovery based on activity data obtained from the e-AR (ear-worn activity recognition) sensor developed at Imperial College London [2] and demonstrates its use to efficiently mine and update a concise variable-resolution synopsis routine for efficient behaviour profiling in a home healthcare environment. Pattern Mining for Routine Behaviour Discovery in Pervasive Healthcare Environments Raza Ali, Mohamed ElHelw, Louis Atallah, Benny Lo and Guang-Zhong Yang T