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Stroke is a brain attack (or infarction of a portion of the brain) caused by the sudden disturbance of blood supply to that area. In recent years, even though the number of stroke-related deaths has been decreasing in Korea, the incidence of stroke is increasing, and the incidence increase with age. The chances of surviving from an acute and sudden infarction are much higher if the elderly people get emergency medical assistance within a few hours of occurrence. Elderly health monitoring and emergency alert system are mentioned as one of the main application areas of pervasive computing and biomedical applications. Moreover, a proactive elderly health monitoring system involves active capture of brain and body movement signals, signal analysis, communication, detection and warning processes. The primary objective of this research will be concerned itself with ambient assisted living issues for the successful detection and generation of alarms in cases of stroke onset, which will allow the timely delivery of medical assistance, to mitigate the long-term effects of these attacks.

The overall aim of this study is to collect stroke assessment methodology for pre-detect the stroke using the wearable devices. The particular objectives of the study were (1) to find various stroke-monitoring methodologies, (2) to compare stroke-monitoring methodologies, (3) to develop a methodology that can pre-detect the stroke. For this purpose, a systematic search was conducted using Google Scholar for studies published from 2010 to 2015. The search was restricted to papers published in English and containing the terms “stroke motion”, “stroke detection”, “stroke application”. The literature survey revealed various stroke detection methods including motion pattern, gait analysis, head impulse test, electrocardiogram, eye movements, muscle activity recognition, and so on.

At present, many automakers are paying attention to develop vehicles with IoT enabled including healthcare, accident prevention, vehicle safety, driver safety, driver and passenger comfort, vehicle monitoring, etc. This paper focused on reviewing the recent developments in driver's health and comfort monitoring through IoT. The literature review was performed with popular search engines/databases like PubMed, ScienceDirect, Elsevier Science, Scopus, and Google Scholar. The search was based on keywords like "Healthcare Monitoring", "Seating Comfort", "IoT developments", "Smart Sensing", "Embedded Sensors", "Wearable Sensors", "in-vehicle smart assistance", "Autonomous Vehicle/Car". Later, the articles were sorted out according to the relevance of this paper's focus (i.e., driver's health and comfort monitoring through IoT) by reading title, abstract, and full article. The results show that there have been many IoT based health and comfort monitoring developments concerning the vehicle. We have classified and summarised the developments as health monitoring through seat pan, seat back, seat belt, steering wheel, eye tracking and face tracking, and comfort monitoring/enhancement through smart seating, smart seat cushion, smart door module, smart air-bag, smart seat belt buckles. This survey would be giving in-depth knowledge on recent developments in the field of automotive and IoT.

Alcohol consumption impairs driving skills and responsible for a high proportion of traffic accidents. This research examined the effects of blood alcohol consumption (BAC) level on driver’s physiological behavior and driving performance under simulated driving environment. The participants consisted of eight healthy young male drivers with an average of eight years driving experience. The scenario considered was 5 min driving on a highway with different speed limits, crossings, and unexpected event. Results showed that statistically significantly decreased alpha and increased theta power frequency was observed with increased BAC level. Heart rate was statistically significantly elevated with increased BAC level. The accident rate and the over speed rate were significantly higher with higher BAC level. Alcohol intoxication affected drivers’ decision-making ability, vision and integrating visual information ability.