# Design and Testing of a Wearable System for Monitoring Car Drivers
## 1 Introduction
On one hand, several studies have focused on analyzing the human body during braking or accidents [7]. On the other hand, many research efforts have been based on designing systems to mitigate these injuries [8]. However, a common starting point for most of these works is the study of the physiological state of the driver, which thus appears to play an essential role in these accidents. Driver physiology is a complex field that encompasses various aspects of human health, including alertness, fatigue, stress, and emotions. When an individual takes the car wheel, their physiological state can vary depending on numerous factors such as their level of rest, emotional state, and overall health. Understanding these aspects is essential for predicting dangerous behaviors on the road or studying the variation of injury risks based on their physiological state. Recent studies have emphasized the importance of real-time  
Figure 1: Phases of neck S-shape motion in a rear impact [2]: (**a**) retraction; (**b**) forward movement; (**c**) belt restraint. monitoring of the driver's physiological state, as it directly influences their driving abilities and responsiveness [9]. One can take the example of fatigue, stress, and drowsiness, which significantly reduce the driver's vigilance, affecting their ability to react quickly to road situations [10]. Indeed, drowsiness while driving is a major problem as it can lead to micro-sleeping, where a driver briefly loses awareness of their surroundings, greatly increasing the risk of serious accidents. Similarly, stressed drivers are more likely to make mistakes such as ignoring a red light or deviating from their car lane, increasing the risk of accidents. Signs of fatigue and stress may be relatively easy to detect, but they can still go unnoticed by the driver themselves. Therefore, it is imperative to design systems capable of real-time monitoring of the driver's physiological state to prevent potential dangers on the road. Identifying the physiological signals associated with emotional states is also addressed in the current research on prevention of car accidents. There are a lot of different emotions that can have a real influence on a driver's behavior. The main emotional states can be recognized in anger, joy, fear, and sadness. Emotions such as anger, fear, sadness, or joy can impair decision-making and increase the risk of accidents [11]. For example, an angry reaction to another driver can lead to aggressive driving, thereby increasing the risk of collisions. Thus, researching the physiological mechanisms underlying these emotional states of the driver is essential for the development of sophisticated monitoring systems [12]. These systems can help anticipate risky behaviors and prevent accidents on the road. The monitoring of drivers' physiological state is a rapidly expanding field, aiming to capture and analyze various aspects of human health and behavior during driving; it is not only limited to detecting fatigue or distraction but also encompasses other emotional and psychological states that can influence road safety. Technological advancements have led to the development of sophisticated sensors capable of measuring a variety of physiological parameters in real time. These sensors are essential for monitoring the driver's physiological state, providing critical data that can help improve road safety. To generate an overview of the primary sensors used in this field, we can refer to the following:  
* Cameras and Microphones: Eye-trackers and facial recognition cameras, along with microphones, analyze the driver's facial and vocal expressions to detect fatigue and emotional states such as anger or frustration. These devices offer valuable insights into how emotional states impact driving behavior [13]. * Contact Sensors: Devices like heart rate (HR) monitors, heart rate variability (HRV) sensors, respiratory rate (RR) monitors, and skin conductance (SC) sensors collect data that are crucial for assessing driver fatigue and mood changes.