# Design and Testing of a Wearable System for Monitoring Car Drivers
## 1 Introduction
These data can indicate the driver's current state and predict potential issues related to fatigue and stress. * Inertial Measurement Units (IMUs): These sensors include accelerometers and gyroscopes that detect driver movements, providing information about movements associated with emotions such as irritation or fear. This information helps in understanding the physical responses related to different emotional states [14]. * Electroencephalography (EEG): EEG sensors measure brain electrical activity, helping to understand patterns associated with various mental states such as happiness, sadness, anger, and mental load. These data are vital for evaluating the driver's cognitive and emotional conditions [15]. The abovementioned sensors can be integrated into the driver's seat or steering wheel, and/or they can be designed as wearable devices like smartwatches, glasses, caps, earpieces, or vests. This integration allows for continuous monitoring of the driver's physiological state, enabling quick detection and response to changes that may affect their safety and that of other road users. Once physiological data are collected by sensors, they need to be analyzed to detect patterns, trends, and anomalies [16]. Data analysis methods often include machine learning techniques, signal processing, and pattern recognition. It is with the help of these EEG signals and pattern recognition that one can establish reliable solutions to detect drowsiness [17]. These methods translate raw data into meaningful information about the driver's health and state of vigilance. Several studies focus on these key factors which highlight variations in heart rate, respiratory rate, and skin conductance as indicators of stress and anxiety in drivers [11]. Significant advances in fatigue detection have also been made through methods with brainwave analysis and the use of convolutional neural networks [12]. Since fatigue and drowsiness are major factors contributing to road accidents, these analysis techniques are significant for accident prevention. From the extensive literature on topics referring to the analysis and design of monitoring systems for safe driving, it is worth noting examples of the variety of proposed solutions using sensorization of drivers and cars that are combined with procedures of data elaboration. In [18], the authors present a monitoring system with IMUs and EMG sensor installed on the head and neck to detect drivers' emotional responses. Reference [19] proposes a detection of abnormal driving by using signals from EMG sensors. In reference [20], a visual detection of drivers is used to evaluate driver distraction by using driver data through neural network algorithms. Driving behavior is formulated in [21] by using signals from distributed sensors that are installed on the driver and on the car. The authors of [22] elaborate on the detection of inattention and drowsiness status of a car driver by using a sensorization of the driver motion and cardiac response. In the current automotive context, it is significant to assess the driver's physiological state, particularly focusing on aggressiveness, as it directly impacts driving safety. Aggressive driving behavior can lead to risky maneuvers, increasing accidents, and endangering both the driver and other road users. Aggressiveness can stem from emotions such as stress, joy, and anger, so that it is important and complementary to identify these emotional triggers of aggressive driving. Therefore, developing a means to effectively monitor and interpret signs of aggressiveness in drivers is useful to enhance road safety. To address this challenge, the objective of this work is to design a portable and discrete system that is capable of properly detecting and analyzing signs of aggressiveness in drivers. Such a system aims to improve driving comfort, safety, and overall road user well-being by providing timely interventions or alerts when aggressive behavior is detected.