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IoT (Internet of things) is considered most innovative technology in smart healthcare monitoring system which is able to show real-time physiological parameters feed data to web cloud, analysis using machine learning, artificial intelligence and big data. Stroke is most deadly diseases and real-time monitoring is desired to detect stroke onset during regular activities. The aim of our study is to develop a Real-time health monitoring system for elderly drivers using air cushion seat and IoT devices in order to detect stroke onset during driving. We have also made a prototype of brain stroke detection system using Quad-chamber air cushion system and IoT devices. This system can measure ECG, EEG, heart rate, seat pressure balance data, face/eye tracking etc. using IoT sensors, compare real-time data with reference data, predict abnormality, generate alarm and send message to relatives and emergency services if any stroke onset happens in order to provide emergency assistance to driver.

Changes in a driver's physiological variables indicate his condition. Knowing how these variables change when driving is important in measuring the driver's mental and physical fatigue caused by driving. In this study, we investigated the relationship between the number of hours of driving and characteristics of heart rate variable (HRV) signal. 5 subjects were asked to drive four different passenger vehicles on an expressway. All tests were repeated twice. The experimental number was 40 times. After 3-hours of driving, differences in the HRV signal characteristics were observed. These indicated that driving fatigue is able to detected through the HRV signal.