## 1 Introduction  
The increasing number of road traffic accidents globally has sparked interest in analyzing the causes and factors for their occurrence, as well as the extent of damage and their consequences. Factors are categorized based on various characteristics, such as road infrastructure, control, and speed limits, technical condition of the vehicle, human factors, and others [1]. Of the human factors, special attention is paid to the medical condition of the driver, drowsiness, fatigue, and use of alcohol and drugs. In the USA, a study published by the NHTSA [2] showed that up to 94% of road accidents are the result of driver errors, which are often caused by their mental and/or physiological state. Fatigue, distraction, strong emotional state (e.g., anger), and overthinking are prerequisites for a high probability of critical driver errors. The final report of the preliminary study SYNCOPE [3] indicated that in Sweden, the main cause of fatal road accidents and about 10% of road deaths is sudden medical illness (SMI) of the driver. SMI is a sudden health disorder that, by its nature, poses a direct threat to life or health and requires prompt, immediate treatment. A study in Spain [4], investigating the prevalence of adverse health conditions among Spanish drivers, found that 42% of 1200 drivers sometimes felt unfit to drive, and 1.8% considered this to happen often. It is possible that the statistics do not include fatal accidents that are due to SMI, because they are not defined as such and accordingly documented. This leads to the assumption that the proportion of accidents caused by SMI could be higher. Furthermore, sudden medical illnesses while driving, such as stroke, heart attack, and arrhythmias, accompanied by dizziness, vertigo, and fainting, are considered major causes of severe road traffic accidents [5; 6]. The occurrence and severity of traffic accidents can be reduced by using a driver monitoring system (DMS) capable of detecting the onset of drowsiness, fatigue, distraction, strong emotional state, and life-threatening medical conditions, even at an early stage. Monitoring of drivers' physiological parameters can be implemented using different measurement methods and sensors comprising wearable devices, such as smart watches, rings, and fitness bands with embedded sensors, wearable sensors for measuring vital biological signals, or sensors and technologies embedded in the vehicle itself. In 2021, [PERSON] [7] presented a review of heartbeat detection systems in automotive applications and addressed the widespread use of smartwatches and smartphones, as well as barriers to their practical use in vehicles. In the conclusion of this review, the author noted that there are no recent examples of systems or devices that assess the driver's condition based solely on heart rate, and that devices that can detect health conditions and abnormalities are expected to emerge as future applications. In 2022, [PERSON] et al. [8] presented a detailed review of driver drowsiness detection systems, describing the main approaches followed in the design of such systems. Based on the type of parameters used to indicate drowsiness, they categorized them into image-based, biological-based, vehicle-based, and hybrid systems. The same year, [PERSON] et al. [9] presented a scoping review of DMSs based on physiological indicators. In particular, they focused on the key physiological indicators applied in the design and development of DMSs related to driver recognition, driver alertness, driver drowsiness, driver fatigue, and drunk driving detection. In 2023, [PERSON] and [PERSON] [10] presented a review of research and applications of emotion recognition based on physiological signals, emphasizing their role and characteristics. Reviewing various applications, the authors found that a single physiological signal contains limited information and has poor representation ability. Usually, two or more physiological signals are needed to implement an emotion recognition system.