Mobile context awareness for managing context healthcare data: a survey (original) (raw)
2017, Journal of Software Engineering & Intelligent Systems
Context awareness was introduced in several fields in routine human activities. Healthcare systems are the most important ones among context-aware applications. The existence and awareness of the context made mobile device users conscious of physical environments or situations. Smart devices, armed with multiple sensors, can sense and react based on their environment, and become context-aware agents. Smart devices are ideal assistants that provide accurate solutions for the critical aspects of healthcare, and medical staff works as well as the closer up to date state of the patient. In addition, hospital's staff members can communicate with the patients reliably and suitably by using smart devices offers. But the applications of healthcare have lack of standardization in handling the context and the perceived sensors data. To perceive the context, we can rely on sensors, which may be physical or virtual. More generally, our research will concern on contextual applications of healthcare that aroused increasingly important interest. To improve the user experience, the advantage of contextual data is aimed to take by context-aware applications. A useful tool to reason about context is proved by using case-based reasoning (CBR) and fuzzy techniques to deal with lack of standardization in handling the context and uncertainty of data. On the other side, it could be interesting to analyse how context reasoning relates to CBR. To assess how CBR can be integrated with contextual information is the primary concern of our research study. So, in this paper, we concrete on the mobile application of healthcare that deals with contextual data by using CBR techniques to analyse the context data. It gives a perfect situation in a right way or prediction for providing accurate solutions for the critical aspect of healthcare and medical staff work. It can overcome the problem of the standardization for dealing with context data.
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