## 1 Introduction
### Stress Detection and Monitoring  
The availability of wearable sensors aimed at determining stress has greatly facilitated the process of data acquisition. Stress detection using wearable devices is typically based on measuring physiological changes that correlate with stress responses. The galvanic skin response (GSR), also known as electrodermal activity (EDA), is well-recognized as a primary indicator of stress [1]. EDA, a measure of skin conductance that increases with stress, or skin resistance that decreases with stress, is one of the most widely studied metrics due to its close relationship with sympathetic nervous system activation. [PERSON] et al. [2] demonstrated that combining wrist-based EDA data with Machine Learning (ML) algorithms provides a robust foundation for stress detection with minimal user burden. The study conducted in [2] demonstrated its effectiveness in identifying stress, achieving an accuracy of 92% using K-Nearest Neighbor (kNN), Naive Bayes, Random Forest, and Support Vector Machine (SVM) based on features extracted from GSR signals. Another significant physiological marker is PPG, which, as shown in [3], enables the detection of psychological stress with accuracies ranging from 86% to 91% using the elastic-net model. Furthermore, ref. [4] explored the use of electrocardiography (ECG) and electroencephalography (EEG) along with EDA signals in combination with machine learning methods to develop an objective stress assessment system. The study reported 84.0% sensitivity, 90.0% specificity, and 86.0% overall accuracy achieved by the SVM classifier in distinguishing whether a person was stressed or not. Another piece of research aimed to establish a practical stress detection framework by integrating EEG and GSR signals and deep learning techniques [5]; it showed the highest AUROC value of 0.954 based on the ResNet-50. However, EEG-based studies are limited in that they can only be performed on an outpatient basis or in the hospital by trained personnel [6]. Most studies today are based on a combined approach to stress diagnostics. This means they use not just one of the physiological parameters studied, but a combination of them, including biochemical biomarkers. Studies by [PERSON] et al. [7] and [PERSON] et al. [2] show that physiological signals such as EDA, HRV, and biochemical markers in sweat can be used to accurately monitor stress levels. According to [8], it is crucial to be able to recognize stress that is not induced in laboratory conditions, but stressful conditions that arise in a person's daily life. The recorded signals can then be supplemented with ML algorithms, a process which provides accurate and continuous stress monitoring and is especially valuable in \"free living\" conditions, when people engage in their daily activities without restrictions [9]. However, the acquisition of chemical biomarkers as well as EEG signals requires the expertise of qualified personnel, which limits the potential of such approaches. Moreover, the fundamental difference between these methods and the one we propose is that we were the first to use the siren signal as a stress factor. The integration of the emerging concept of data transmission (IoT) with wearable stress detection systems expands the possibilities of remote monitoring and data-driven interventions. For example, [PERSON] et al. [10; 11] showed the possibility of differentiating between stress-induced and non-stress-induced EDA, which can improve the specificity of stress monitoring in multi-context settings such as clinical and everyday scenarios. Therefore, our study further advances the research works in [10; 11] by opening new avenues for studying stress factors in real-world conditions. The development of electronic skin (e-skin) devices has further advanced stress monitoring capabilities. [PERSON] et al.