Keywords

1 Introduction

Aging results from increasing longevity, and most importantly, declining fertility [1]. Population aging is taking place in nearly all the countries of the world. As age increases, older drivers become more conservative on the road. Age-related decline in cognitive function threatens safety and quality of life for an elder. As the population in the developed world is aging, so the number of older drivers is increasing [2,3,4]. Research on age-related driving concerns has shown that at around the age of 65 drivers face an increased risk of being involved in a vehicle crash. Three behavioral factors, in particular, may contribute to these statistics: poor judgment in making left-hand turns; drifting within the traffic lane, and decreased ability to change behavior in response to an unexpected or rapidly changing situation [5,6,7,8].

Stroke is the sudden death of brain cells due to lack of oxygen, caused by blockage of blood flow to the brain or rupture of blood vessels [9, 10]. Stroke is the second leading cause of death above the age of 60 years, and its population is increasing [10, 11]. The stroke symptoms are a weakness in the arm or leg or both on the same side, loss of balance, sudden headache, dizziness, coordination problems, vision problems, difficulty in speaking, and weakness in the face muscle [12,13,14]. Sudden stroke onset during driving poses a serious threat to the other drivers and the general public.

The Internet of Things (IoT) plays an important role in the development of connected vehicles, which offers cloud connectivity, vehicle-to-vehicle connectivity, smartphone integration, safety, security, and healthcare services. Recent developments show already automakers are paying attention to develop cars that could monitor driver’s health status. Both luxury automakers and key global original equipment manufacturers are integrating healthcare services into their next-generation products [15]. The purpose of this study is to successfully detect and generate alarms in cases of stroke onset while driving. This paper focused on briefly explaining the conceptual idea and related information of the elderly healthcare services in-vehicle using IoT.

2 Framework of Health Monitoring Service

Hyper-connected self-machine learning engine controls an elderly health monitoring system (Fig. 1). The components of the system are big data, real-time data monitoring, network security, and self-learning engine. The knowledge base would have risk factors, medical health records, psychological factors, gait and motion patterns, and bio-signals. The old peoples’ activities, physiological, and bio signals are monitored in real-time through wearable sensors. The self-machine learning engine would include multi-model learning and model generator. If the proposed system predicts stroke symptom above 90%, it will generate an alarm to family, the victim, people around the victim, and healthcare professionals. Then the victim will get the timely medical assistance.

Fig. 1.
figure 1

Components of the proposed elderly health monitoring system

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Our proposed system is designed to get the rapid measurements required to monitor health status in a critical situation and in a cost effective way. The health status likes blood pressure, body temperature, heart rate, muscle activity, human brain activity, cardio activity, motion tracking, etc. are all very important to track down the healthcare status [16,17,18]. An IoT-based system is drastically reducing the costs and improving health by increasing the availability and quality of care [19,20,21,22]. Also, advancement in mobile gateway integrated with healthcare sensor can preferably be offered on a small, wearable and portable device, suitable for daily and continuous use, such as a smartphone or personal digital assistant [23,24,25,26]. Therefore, we employed specialized wearable sensors to monitor senior citizens physical and physiological activities. The portable sensors include motion sensors, EMG sensors, ECG sensor, and insole type foot sensors.

Already there are many developments in the wearables and embedded sensors to measure physiological and bio signals. Some of them are summarized here. An ECG monitoring system was introduced [27] comprised of six wearable textile-based electrodes are capable of providing e-health service via Bluetooth. An automotive seat [28] developed by Faurecia detects traveler’s heart rate and breathing rhythm through unique types of embedded sensors. The Nottingham Trent University [29] developed a car seat with a capacitive sensor to detect traveler’s heart rhythm. IPPOCRTE [30] proposed a steering wheel could measure vital parameters including body temperature, ECG, eye gaze, and pulse rate. Toyota also developed a smart steering wheel to monitor the driver’s ECG [31].

3 Summary

The purpose of this study is to successfully detect and generate alarms in cases of sudden stroke onset while driving. The conceptual design of the elderly health monitoring system presented in this paper. The proposed system could predict stroke symptoms and generate an alarm. Thereby, the stroke victim will get the timely medical assistance.