Complex Event Processing based Remote Health Monitoring System

The existing Remote Health Monitoring System (RHMS) is not fast enough to detect the abnormality as it uses batch processing to detect abnormality in the vital parameters of the patient and it doesn't scale well. CEP uses event driven approach and correlates different sensor streams with spatiotemporal constraints for abnormality detection. This paper proposes a CEP based Remote Health Monitoring System named (CRHMS). The proposed CRHMS uses Biosensors (Heart rate, Respiration Rate, Blood Pressure and ECG) to collect the vital parameters and environmental sensors (Accelerometer, Global Positioning System (GPS)) for context identification of the elderly patient who is home alone. These sensor parameters are collected on android phone and sent as a stream to the proposed system to identify the abnormalities in vital signs and generate alert. The proposed system has been implemented using Drools Fusion engine and validated for several patients. The performance of the proposed CRHMS is compared with existing batch processing system. It is found that the proposed system has improvement over Batch Processing System in terms of Event processing time. Also, the developed system is made adaptive by providing dynamic learning support using hot rule addition.