# Individualized Stress Mobile Sensing Using Self-Supervised Pre-Training
## 2 Related Work  
### Stress Assessment Using Biosignals  
The hyperactivity of the nervous system is associated with chronic stress and is responsible for its wide range of adverse effects on health and behavior. Stress and brain activity are closely correlated [9]. [PERSON] et al. showed that single-lead ECGs can be used to identify rhythms using an end-to-end deep learning method. They demonstrated that the 1D CNN model architecture can be successfully applied towards making predictions from biosignal data without manual feature engineering and feature selection [6]. Variations in the skin's electrical characteristics are reflected by EDA, making it a helpful stress indicator for the nervous system. Sweating is a natural response to stressful situations and may further enhance the skin's conductivity [10]. [PERSON] et al. described a stress-prediction framework that enables consumers to receive feedback on their stress levels through speech-enabled consumer devices like smartphones and smart speakers [11]. EDA sensors are located on the hands, whereas ECG sensors are located on the chest. Data from the EDA sensors are wirelessly transferred to the ECG sensor through WiFi. This method attempts to improve stress prediction by capturing two EDA channels to eliminate motion artifacts. The designed sensor yields excellent linearity and jitter performance and exhibits minimal delays during wireless transmission. [PERSON] et al. studied the possibility of detecting stress using EDA data acquired from wearable sensors [12]. The study made use of two publicly accessible datasets: VerBIO [13] and WESAD [14], both of which comprise EDA signals from research-grade wearable sensors. The experimental results demonstrated that Random Forests differentiate stress from non-stress conditions with an accuracy of 85.7%, highlighting the potential for wearable devices with EDA sensors to anticipate stress. The study also evaluated the dependability of training with extracted statistical and EDA-related features from EDA signals, discovering no consistent trend between employing complete features and chosen features. The findings indicated the effectiveness of placing EDA sensors into wearable devices for stress prediction, instilling confidence that incorporating EDA sensors into smartwatches is a potential option for future product development. In addition to research-grade equipment, EDA is also frequently measured using consumer wearable devices. Using low-resolution electrodermal activity (EDA) data from consumer-grade wearable devices, [PERSON] et al. explored the stress prediction capabilities of user-dependent and user-independent models [15]. This work revealed that stress-prediction models employing low-resolution EDA data from wrist-worn and finger-mounted sensors have high balanced accuracy scores (66.10% to 100%). This finding indicated that low-resolution EDA signals from consumer-grade devices may be used to efficiently create user-dependent stress-prediction models, enabling the collection of in-the-wild data for mental health tracking and analysis. EDA is often used in conjunction with other biosignals. [PERSON] et al. suggested a way to detect stress by using sensors from the Empatica E4 device used in the WESAD [14] dataset to measure blood volume pulse (BVP) and electrodermal activity (EDA) [16]. The suggested neural network model was 96.26% accurate. The study also called for more research to test the method's usefulness and general flexibility. [PERSON] et al. [17] explored the capacity of electrodermal activity (EDA) to precisely categorize mental states, including relaxation, arousal, and stress, in addition to self-perceived stress and arousal, utilizing a three-phase modified MIST test. The research demonstrated high levels of accuracy in identifying these mental states, indicating that EDA has the potential for long term evaluation of stress and arousal in the workplace.