Framework of health monitoring service for the elderly drivers community

Abstract

Stroke is the most common causes of death in the elderly community. It is the second leading cause of death, accounting for a 6.24 million deaths in 2015. The stroke population, as well as world population, is aging. Stroke onset while driving threatens driver and public safety on roads. Already automakers are paying more attention to developing cars that could measure and monitor drivers’ health status to protect the elderly population. The automobile is rapidly becoming a “thing” in the Internet of Things (IoT). The purpose of this study is to successfully detect and generate alarms in cases of stroke onset while driving. The goal is achieved through the development of an elderly health monitoring system, which is controlled by hyper-connected self-machine learning engine. The components of the system are big data, real-time data monitoring, network security, and self-learning engine. A proactive elderly health monitoring system is involved with the active capture of the brain, cardio and body movement signals, signal analysis, communication, detection and warning process. This system has been considered as one of the main application areas of pervasive computing and biomedical applications. The method mentioned above and its frameworks will be discussed in this paper.