Poster
in
Workshop: Learning from Time Series for Health
Dissecting In-the-Wild Stress from Multimodal Sensor Data
Stress is associated with numerous chronic health conditions (both physical and mental). However, the effect of stress on individuals is understudied, leaving crucial questions unanswered. In particular, how variable is stress within and among individuals? In this work, we unveil preliminary findings from a major data collection effort from Digital Health Technologies (DHTs, such as smart rings and smartphones) and provide insights into stress in-the-wild. We use causal discovery to learn robust representations of stress in this population. Our findings reveal high levels of inter- and intra-individual heterogeneity in stress. This study is an important first step in better understanding potential underlying processes reflective of stress in individuals.