Ontology Based Context Fusion for Behavior Analysis and Prediction
Current healthcare systems facilitate patients in provision of healthcare services by using their context information. However, the problem is that the context information received from various sources is of heterogeneous nature which is not useful for conventional systems. To overcome this issue, we propose an ontology-based context fusion framework in this research that fuses the related and relevant context information collected about the patient’s daily life activities for better understanding of patient’s situation and behavior. The fused context information is logged using ontological representation in Life Log deployed on cloud server. On top of the Life Log, behavior analysis and prediction services are developed to analyze the behavior of the patient and provide better healthcare, wellness, and behavior prediction services. System execution flow is demonstrated using a running case study that shows how the overall process is initialized and performed.
Keywordsu-Healthcare Lifestyle Activity recognition Ontology Context-awareness Context fusion
This research was supported by the MSIP (Ministry of Science, ICT & Future Planning), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2014-(H0301-14-1003)).
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