BDCaM: Big Data for Context-Aware Monitoring—A Personalized Knowledge Discovery Framework for Assisted Healthcare

Context-aware monitoring is an <italic>emerging technology</italic> that provides real-time <italic>personalised health-care</italic> services and a rich area of <italic>big data</italic> application. In this paper, we propose a knowledge discovery-based approach that allows the <italic>context-aware system</italic> to adapt its behaviour in runtime by analysing large amounts of data generated in <italic>ambient assisted living (AAL)</italic> systems and stored in <italic>cloud repositories</italic>. The proposed <bold>BDCaM</bold> 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 <italic>decision-making</italic> processes for the patient. <italic> A use case</italic> 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 <italic>anomalous situations</italic> for different types of patients. The accuracy and efficiency obtained for the implemented case study demonstrate the effectiveness of the proposed model.