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Enhanced Complex Event Processing Framework for Geriatric Remote Healthcare
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Author(s): V. Vaidehi (VIT University, India), Ravi Pathak (Striim Inc., India), Renta Chintala Bhargavi (VIT University Chennai, India), Kirupa Ganapathy (Saveetha University, India), C. Sweetlin Hemalatha (VIT University, India), A. Annis Fathima (VIT University, India), P. T. V. Bhuvaneswari (Madras Institute of Technology, India & Anna University, India), Sibi Chakkaravarthy S. (Madras Institute of Technology, India & Anna University, India)and Xavier Fernando (Ryerson University, Canada)
Copyright: 2018
Pages: 32
Source title:
Handbook of Research on Investigations in Artificial Life Research and Development
Source Author(s)/Editor(s): Maki Habib (The American University in Cairo, Egypt)
DOI: 10.4018/978-1-5225-5396-0.ch016
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Abstract
Advances in information and communication technology (ICT) have paved way for improved healthcare and facilitates remote health monitoring. Geriatric remote health monitoring system (GRHMS) uses WBAN (wireless body area network) which provides flexibility and mobility for the patients. GRHMS uses complex event processing (CEP) to detect the abnormality in patient's health condition, formulate contexts based on spatiotemporal relations between vital parameters, learn rules dynamically, and generate alerts in real time. Even though CEP is powerful in detecting abnormal events, its capability is limited due to uncertain incoming events, static rule base, and scalability problem. To address the above challenges, this chapter proposes an enhanced CEP (eCEP) which encompasses augmented CEP (a-CEP), a statistical event refinement model to minimize the error due to uncertainty, dynamic CEP (DCEP) to add and delete rules dynamically into the rule base and scalable CEP (SCEP) to address scalability problem. Experimental results show that the proposed framework has better accuracy in decision making.
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