all AI news
MoodCapture: Depression Detection Using In-the-Wild Smartphone Images
Feb. 27, 2024, 5:47 a.m. | Subigya Nepal, Arvind Pillai, Weichen Wang, Tess Griffin, Amanda C. Collins, Michael Heinz, Damien Lekkas, Shayan Mirjafari, Matthew Nemesure, George
cs.CV updates on arXiv.org arxiv.org
Abstract: 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 …
abstract arxiv cs.cv cs.hc daily depression detection images major novel people photos smartphone smartphones type
More from arxiv.org / cs.CV updates on arXiv.org
Compact 3D Scene Representation via Self-Organizing Gaussian Grids
2 days, 17 hours ago |
arxiv.org
Fingerprint Matching with Localized Deep Representation
2 days, 17 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne