MoodCapture：使用真实环境智能手机图像进行抑郁症检测
MoodCapture: Depression Detection Using In-the-Wild Smartphone Images
AI总结：
MoodCapture 利用智能手机前置摄像头在真实环境中自动采集的超过 125,000 张图像，结合面部关键点和随机森林模型，实现对抑郁症的分类与 PHQ-8 分数预测，并分析了用户隐私关切。
摘要：
MoodCapture 提出了一种新颖的方法，通过人们在日常生活中由智能手机前置摄像头自动拍摄的图像来评估抑郁症。我们在 90 天内从 N=177 名被诊断患有重度抑郁障碍的参与者那里收集了超过 125,000 张真实环境照片。图像是在参与者回答 PHQ-8 抑郁症调查问题“我感到情绪低落、抑郁或绝望”时自然拍摄的。我们的分析探索了重要的图像属性，例如角度、主色、位置、物体和光照。我们表明，使用面部关键点训练的随机森林可以将样本分类为抑郁或非抑郁，并有效预测原始 PHQ-8 分数。我们的事后分析通过消融研究、特征重要性分析和偏差评估提供了若干见解。重要的是，我们评估了用户对使用 MoodCapture 基于分享照片来检测抑郁症的担忧，为隐私问题提供了关键见解，这些见解将为未来基于真实环境图像的 mental health 评估工具的设计提供参考。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
