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

TL;DR

This paper investigates digital biomarkers derived from wearable devices to classify stress levels using machine learning, achieving high accuracy in daily-life scenarios by integrating multimodal data and feature selection.

Contribution

It introduces a novel approach to interpret stress biomarkers from multimodal wearable data and improves stress level classification accuracy with feature selection and class imbalance handling.

Findings

Achieved 85% overall accuracy in stress level classification.

Identified key stress biomarkers from multimodal wearable data.

Performed better than related studies in daily-life stress recognition.

Abstract

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.