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With the development of wearable technologies, a new kind of healthcare datahas become valuable as medical information. These data provide meaningfulinformation regarding an individual's physiological and psychological states,such as activity level, mood, stress, and cognitive health. These biomarkersare named digital since they are collected from digital devices integrated withvarious sensors. In this study, we explore digital biomarkers related to stressmodality by examining data collected from mobile phones and smartwatches. Weutilize machine learning techniques on the Tesserae dataset, precisely RandomForest, to extract stress biomarkers. Using feature selection techniques, weutilize weather, activity, heart rate (HR), stress, sleep, and location(work-home) measurements from wearables to determine the most importantstress-related biomarkers. We believe we contribute to interpreting stressbiomarkers with a high range of features from different devices. In addition,we classify the $5$ different stress levels with the most important features,and our results show that we can achieve $85\%$ overall class accuracy byadjusting class imbalance and adding extra features related to personalitycharacteristics. We perform similar and even better results in recognizingstress states with digital biomarkers in a daily-life scenario targeting ahigher number of classes compared to the related studies.

Extracting Digital Biomarkers for Unobtrusive Stress State Screening from Multimodal Wearable Data