  With the development of wearable technologies, a new kind of healthcare data
has become valuable as medical information. These data provide meaningful
information regarding an individual's physiological and psychological states,
such as activity level, mood, stress, and cognitive health. These biomarkers
are named digital since they are collected from digital devices integrated with
various sensors. In this study, we explore digital biomarkers related to stress
modality by examining data collected from mobile phones and smartwatches. We
utilize machine learning techniques on the Tesserae dataset, precisely Random
Forest, to extract stress biomarkers. Using feature selection techniques, we
utilize weather, activity, heart rate (HR), stress, sleep, and location
(work-home) measurements from wearables to determine the most important
stress-related biomarkers. We believe we contribute to interpreting stress
biomarkers 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 by
adjusting class imbalance and adding extra features related to personality
characteristics. We perform similar and even better results in recognizing
stress states with digital biomarkers in a daily-life scenario targeting a
higher number of classes compared to the related studies.
