A Framework for Stroke Prevention Using IoT Healthcare Sensors

Abstract

Stress and depression are negative emotions which could have impact on the persons’ life when repeated for long time. There are several systematic studies showing that these emotions promote chronic diseases, such as stroke, hypertension, heart attack, high blood pressure (BP), digestive diseases, and cardiovascular diseases, reduce immune functions, and so on. Therefore, emotions analysis and management represent most attractive sides of recent emerging of internet of medical things (IoMT) applications and smart wearable sensors. The work in this paper aims to use IoMT technology to recognize stress level using measured data from wearable sensors and show how stress may pose risks on the human health and may lead to chronic diseases such as stroke. Moreover, an effective system called—EmoStrokeSys—has been proposed for health monitoring that combines three wearable sensors: Glucometer sensor to measure glucose level, iHealth wireless blood pressure to measure BP and heart rate, and GSR sensor to detect stress level. The main goals of the proposed EmoStrokeSys system are to control all outcomes of the mentioned parameters under stress situation and minimize the risk of failure by closely monitoring an individual level with regular checkups for these parameters. The measured data from wearable sensors has found significant effects of continuous chronic stress which could reverse changes in BP, blood glucose, and heart rate and may lead to stroke.