IMC Lab

Stress is known to stimulate physiological responses such as heart activity changes and sweat, and such events can be detected using wearables such as smartwatches and chest sensors. Moreover, smartphones give access to rich situational contexts and allow stress detection research to move towards uncontrolled environments. Our methods incorporate collection of situational contexts and physiological sensing to accurately predict daily life stress, which is further used for Digital Therapeutics (DTx) such as timely intervention suggestions to prevent health complications by managing stress levels. In the future, alongside the health benefits, this research direction can help individuals optimize their daily stress levels and enhance productivity by “controlling” their stress levels. Depression is a major public health issue, often with a chronic course and poor prognosis. Traditional methods of estimating depression are time-consuming and costly, and may require professional involvement. Passive sensing using mobile and wearable devices can track daily activities and routines, providing digital behavior markers related to depression. Our work provides a systematic data processing pipeline that leverages mobile passive data for the clinical detection and forecasting of depression and depressive moods. We analyze a wide range of digital behavior markers related to depression, including physical movement, social interactions, and daily activities. By integrating these digital behavior markers, we perform comprehensive and accurate assessment of depression, allowing for personalized interventions and long-term symptom management. Traditional in-person counseling encounters limitations in terms of accessibility, flexibility, and social stigma. Additionally, low mental health l