# Vehicular Alarm System Using mHealth Data and Lightweight Security Algorithms
## 2 Related Works  
Many studies into vehicle road accidents have been conducted and have focused on the aspects surrounding the circumstance or accidental outcomes of the incident as opposed to addressing the viewpoint of safe vehicle operation. Many factors such as the driver's training, speed judgment, substance or alcohol consumption, hours driven, health, stress from driving, and other lifestyle factors have been studied in the literature. Of these, the driver's health, their speed judgment, and training are the main contributing factors in road accidents. Studies have shown that fatigue is a main contributing factor in two-thirds of accidents and is therefore of importance in safety considerations [4]. Regardless of a driver's level of training in vehicle operation, fatigue impairs speed judgment and is related to the driver's health. In general, long distance drivers are referred to as over the road (OTR) drivers or long-haul truck drivers who drive for long periods of time with minimal resting periods, e.g., 5.5 h with 15 min of rest, 8 h for half an hour and 11 h with a 1-h break [5]. When driving, drivers are in a seated posture and are relatively sedentary with little movement as compared to being out of the driver's seat. Therefore, health-related data (HD) such as heart rate (HR), respiration rate (RR), blood pressure (BP),  
Figure 1: Integrated mHealth and vehicular information system. body temperature (BT), Electrocardiology (ECG), Electroencephalogram (EEG), Electromyography (EMG), and Blood Oxygen are expected to have little variation over this long duration and may show consistency. In addition, there may be some unique patterns that emerge with little variance in the driver's health data, which can be implicated in the detection of emergency situations by integrating evolving and emerging technologies such as the mHealth network, personal health devices (PHD), biosensors, wearables, contactless sensing for vehicular applications, and emergency alarm systems. Sensors are now evolving to accommodate requirements from health and vehicular applications with increased compactness, efficient computation, and data transmission capabilities, and overall increases in quality, accuracy, and cost affordability. Wearable devices provide motion tracking and health-related data collection such as measuring human movements of falling, sitting, walking, and running to encourage users to move and exercise as well as monitoring the posture. These are used with smartphones, which transmit data to cloud servers or other networks like vehicular application systems. PHD sensors collect health data measurements such as vital signs as well as chronic disease related devices, which may include implanted sensors and devices including pacemakers. Sensors for vehicular applications have been used for fuel control, parking, safety, in-cabin convenience, speed monitoring, oxygen sensing, and engine monitoring. For example, a gas composition sensor along with humidity and temperature sensors checks air quality, monitors the air intake, and adjusts the heating, ventilation, and air conditioning (HVAC) system as required. However, health applications such as monitoring the driver's health data and notifying when there is an imminent risk of falling asleep have not been fully addressed. Vehicular applications can also detect early or real-time warnings for unexpected driving emergencies such as engine failure, jammed accelerator, shattered windscreen, tire blowouts, or animals causing a collision. Due to the evolution of sensor devices and emerging technologies of vehicular and health informatics, it is feasible to sense data, collect, transfer, process, and determine an action in real-time. Furthermore, it is possible to send an alarm notification to the driver and their head office before a potential health condition deterioration occurs by analyzing health data against a prescribed health threshold index of the user [6].