IoT-Fog-Based Healthcare Framework to Identify and Control Hypertension Attack

Hypertension is a chronic disease causing risk of different types of disorders, such as hypertension attacks, cerebrovascular attacks, kidney failure, and cardiovascular diseases. To prevent such risks, statistics related to hypertension have to be monitored and analyzed in real-time. Internet of Things (IoT)-assisted fog health monitoring system can be used to monitor blood pressure (BP) and to diagnose the stage of hypertension in real-time. In this paper, IoT-fog-based healthcare system is proposed for continuous monitoring and analysis of BP statistics to predict hypertensive users. The proposed system initially identifies the stage of hypertension on the basis of user's health parameters collected using IoT sensors at fog layer. After identifying the hypertensive stage, artificial neural network is used for predicting the risk level of hypertension attack in users at remote sites. The vital point of this paper is to continuously generate emergency alerts of BP fluctuation from fog system to hypertensive users on their mobile phones. Finally, analysis results and compiled medical information of each user are stored on cloud storage for sharing with domain experts, such as clinicians, doctors, and personal caregivers. The temporal information generated from fog layer can be utilized for providing precautionary measures and suggestions well on time for patients' wellness. Experimental results reveal that proposed framework achieves low response time, high accuracy, and bandwidth efficiency.