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Issue No. 04 - Oct.-Dec. (2017 vol. 5)
ISSN: 2168-7161
pp: 628-641
Abdur Rahim Mohammad Forkan , School of Computer Science & IT, RMIT University, Melbourne, Vic, Australia
Ibrahim Khalil , School of Computer Science & IT, RMIT University, Melbourne, Vic, Australia
Ayman Ibaida , School of Computer Science & IT, RMIT University, Melbourne, Vic, Australia
Zahir Tari , School of Computer Science & IT, RMIT University, Melbourne, Vic, Australia
ABSTRACT
Context-aware monitoring is an emerging technology that provides real-time personalised health-care services and a rich area of big data application. In this paper, we propose a knowledge discovery-based approach that allows the context-aware system to adapt its behaviour in runtime by analysing large amounts of data generated in ambient assisted living (AAL) systems and stored in cloud repositories. The proposed BDCaM model facilitates analysis of big data inside a cloud environment. It first mines the trends and patterns in the data of an individual patient with associated probabilities and utilizes that knowledge to learn proper abnormal conditions. The outcomes of this learning method are then applied in context-aware decision-making processes for the patient. A use case is implemented to illustrate the applicability of the framework that discovers the knowledge of classification to identify the true abnormal conditions of patients having variations in blood pressure (BP) and heart rate (HR). The evaluation shows a much better estimate of detecting proper anomalous situations for different types of patients. The accuracy and efficiency obtained for the implemented case study demonstrate the effectiveness of the proposed model.
INDEX TERMS
Context, Medical services, Big data, Sensors, Cloud computing, Monitoring, Data models
CITATION

A. R. Forkan, I. Khalil, A. Ibaida and Z. Tari, "BDCaM: Big Data for Context-Aware Monitoring?A Personalized Knowledge Discovery Framework for Assisted Healthcare," in IEEE Transactions on Cloud Computing, vol. 5, no. 4, pp. 628-641, 2017.
doi:10.1109/TCC.2015.2440269
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