MoodCapture: Depression Detection Using In-the-Wild Smartphone Images
This addresses mental health monitoring for individuals with depression, but it is incremental as it applies existing machine learning methods to a new domain-specific dataset.
The paper tackles depression detection by analyzing over 125,000 smartphone images captured in daily life from 177 participants with major depressive disorder, showing that a random forest model using face landmarks can effectively classify depression status and predict PHQ-8 scores.
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.