Intelligent hybrid remote patient-monitoring model with cloud-based framework for knowledge discovery

Abstract

This paper proposes an intelligent hybrid context-aware model for patients under supervision at home that adopts a hybrid architecture with both local and cloud-based components. The cloud-based portion of the model facilitates storing and processing the big data generated by ambient assisted living systems that are used to monitor patients suffering from chronic diseases in their homes, particularly the elderly. The local portion of the model monitors patients in the event of internet disconnections or any other failure in the cloud system. The proposed model utilises context-aware techniques by monitoring physiological signals, ambient conditions, and patient activities simultaneously to derive the real-time health status of the patient. Experimental results demonstrate the effectiveness of our proposed model for monitoring patients and accurately detecting emergencies in imbalanced datasets through a case study on patients suffering from blood-pressure disorders.