# Individualized Stress Mobile Sensing Using Self-Supervised Pre-Training
## 1 Introduction  
Chronic stress can drastically affect an individual's health across several dimensions. Continuous stress increases the risk of cardiovascular disease, hypertension, reduced immunity, and cancer [1]. Despite these detrimental physical effects, stress is often left unmanaged. A growing body of work in the field of human-computer interaction (HCI) is focusing on mobile sensing of stress using signals from wearable consumer devices [2]. Real-time detection of stress via wearable devices can enable stress interventions, which can provide therapeutic support via immediate prompting when stress is detected. Until recently, these mobile sensing methods of subjective outcomes have required large amounts of training data from each user, making the deployment of such models infeasible in practice. We present a novel methodology for personalizing mobile sensing models using a minimal number of human labels. Consumer wearable devices offer a range of biosignal measurements, which may include, but are not limited to, electrodermal activity (EDA), electrocardiograms (ECGs), electromyography (EMG), respiration rate, and galvanic skin resistance (GSR). EDA isa physiological measurement of the electric current that flows through the skin. EDA is particularly sensitive to changes in skin ionic conductivity caused by even minor perspiration that is not visible on the surface of the skin [3]. In comparison to other physiological measurements like heart rate variations and blood pressure, EDA is a robust way to gauge physiological arousal and, by extension, the response to stress. In the field of affective computing, EDA is widely recognized as a key marker of physiological arousal and stress responses [4]. Therefore, an EDA-based stress quantification approach was the primary focus of this investigation. Deep neural networks (DNNs) have been a driving force behind major machine learning progress in recent years [5]. DNNs are multi-layer computational models that may learn progressively complex and high-level representations of the input data to accomplish a prediction goal. DNNs are an especially promising solution for analyzing biosignals due to their ability to spot patterns and learn meaningful features from unprocessed data inputs without requiring extensive data preprocessing or manual feature engineering [6]. Creating a stress prediction model using biosignal data that is generalizable is impractical due to the varying physiological responses to stress among individuals. As a result, stress-monitoring systems must possess the flexibility to accommodate these differences [7]. Rather than building a one-size-fits-all stress-prediction model, we propose a novel approach aimed at developing individual-specific models, with the goal of achieving greater personalization. Such models can enable precision health mobile sensing systems to reach clinically useful performance. A major hurdle in developing personalized mobile sensing models through biosignal data is the procurement of adequate supervisory data (i.e., ground truth labels) to train DNNs, which require large training sets for the training process to converge. Self-reported stress labels are often generated by laborious and faulty manual human labeling owing to human subjectivity and weariness [8]. Current research on EDA-based stress assessment tends to focus on supervised deep learning. Achieving clinically useful model performance is hindered, however, by the fact that traditional supervised learning does not make use of all of the unlabeled data that are available. Individuals who use wearable devices have copious amounts of unlabeled longitudinal body signal data, but only a few labels to indicate important health events. These labels are used for training machine learning models to recognize the events of interest. To bridge this gap, we propose the use of self-supervised Learning (SSL) to learn neural network weights, which are optimized to make predictions about the baseline temporal dynamics of a user's biosignals.