Machine learning for well-being assessment: Exploring digital biomarkers

This thesis investigates the potential of wearable sensors and self-reported questionnaires by exploring different factors of human well-being using digital biomarkers. The primary goal is to identify and validate these biomarkers, comparing them against traditional psychological studies. Various machine learning models are trained and validated for analyzing different well-being factors, including sleep, mental well-being (stress, anxiety, depression, positive and negative affect), and academic achievement. Two extensive datasets are utilized in these explorations: NetHealth, which is collected from more than 700 college students over 4 years, and Tesserae collected over a year of 757 office workers. Advanced techniques, incorporating the analysis of time-lagged data to capture temporal patterns and multi-task learning, are employed to unravel complex relationships among well-being parameters. The research unfolds systematically, progressing from single well-being factor exploration to time-based and multi-task methodologies. This thesis emphasizes the importance of incorporating temporal dimensions and multi-task learning strategies for a more comprehensive understanding of well-being and the factors influencing it. Our findings offer valuable insights into the identification of reliable biomarkers and the relationships between various well-being aspects within two different target groups of university students and office workers. It aims to provide a new perspective, moving beyond single-factor exploration to enhance the comprehensiveness and applicability of well-being studies.