MoodCapture presents a novel approach that assesses depression based on images automatically captured from the front-facing camera of smartphones as people go about their daily lives. We collect over 125,000 photos in the wild from N=177 participants diagnosed with major depressive disorder for 90 days. Images are captured naturalistically while participants respond to the PHQ-8 depression survey question: \textit{``I have felt down, depressed, or hopeless''}. Our analysis explores important image attributes, such as angle, dominant colors, location, objects, and lighting. We show that a random forest trained with face landmarks can classify samples as depressed or non-depressed and predict raw PHQ-8 scores effectively. Our post-hoc analysis provides several insights through an ablation study, feature importance analysis, and bias assessment. Importantly, we evaluate user concerns about using MoodCapture to detect depression based on sharing photos, providing critical insights into privacy concerns that inform the future design of in-the-wild image-based mental health assessment tools.
@article{arxiv.2402.16182,
title = {MoodCapture: Depression Detection Using In-the-Wild Smartphone Images},
author = {Subigya Nepal and Arvind Pillai and Weichen Wang and Tess Griffin and Amanda C. Collins and Michael Heinz and Damien Lekkas and Shayan Mirjafari and Matthew Nemesure and George Price and Nicholas C. Jacobson and Andrew T. Campbell},
journal= {arXiv preprint arXiv:2402.16182},
year = {2024}
}