Multimodal sensor-based Machine Learning for Mental Health

Digital phenotyping and machine learning technologies have shown the potentials to measure objective behavioral and physiological markers, provide risk assessment for people who might have a high risk of poor mental health and wellbeing, and help better decisions or behavioral changes to support health and wellbeing. I will introduce a series of studies, algorithms, and systems we have developed for measuring, predicting, and supporting personalized health and wellbeing. I will also discuss challenges, learned lessons, and potential future directions in mental health and wellbeing research.