Smartphone-Based Psychological Sensing: A Large-Scale Study on the Impact of Extreme Isolation

Abstract

The COVID-19 pandemic and associated isolation measures have greatly impacted mental health, especially among students. Previous attempts at using mobile sensors to analyze users' emotional states faced barriers including insufficient data and limited modalities. This study aims to address these limitations and derive insights on psychological changes under extreme isolation. We collected a large-scale multivariate dataset from 725 undergraduate students during the complete COVID-19 campus lockdown period. To our knowledge, this is the largest dataset on this population during an extended isolated period. Features were engineered from mobile sensor data to capture modalities including physical activity, sleep patterns, and social interaction. Additionally, self-reported assessments related to mental health conditions were compiled. This rich dataset was leveraged to develop a machine learning model based on autoencoders to detect emotional states. Comprehensive experiments indicate the model can accurately predict mental health changes using mobile sensor data. Our work has unique contributions in collecting large-scale isolated data, engineering informative modalities for modeling mental health, and providing a validated detection method. This can support rapid screening and intervention for mental health crises, especially those emerging from extreme events. The dataset and models open promising directions for big data analytics in mobile health and psychological research.