Abstract
Social Anxiety Disorder (SAD) is a widespread mental health condition, yet its lack of objective markers hinders timely detection and intervention. While previous research has focused on behavioral and non-verbal markers of SAD in structured activities (e.g., speeches or interviews), these settings fail to replicate real-world, unstructured social interactions fully. Identifying non-verbal markers in naturalistic, unstaged environments is essential for developing ubiquitous and non-intrusive monitoring solutions. To address this gap, we present AnxietyFaceTrack, a study leveraging facial video analysis to detect anxiety in unstaged social settings. A cohort of 91 participants engaged in a social setting with unfamiliar individuals and their facial videos were recorded using a low-cost smartphone camera. We examined facial features, including eye movements, head position, facial landmarks, and facial action units, and used self-reported survey data to establish ground truth for multiclass (anxious, neutral, non-anxious) and binary (e.g., anxious vs. neutral) classifications. Our results demonstrate that a Random Forest classifier trained on the top 20% of features achieved the highest accuracy of 91.0% for multiclass classification and an average accuracy of 92.33% across binary classifications. Notably, head position and facial landmarks yielded the best performance for individual facial regions, achieving 85.0% and 88.0% accuracy, respectively, in multiclass classification, and 89.66% and 91.0% accuracy, respectively, across binary classifications. This study introduces a non-intrusive, cost-effective solution that can be seamlessly integrated into everyday smartphones for continuous anxiety monitoring, offering a promising pathway for early detection and intervention.
Abstract (translated)
社交焦虑障碍（SAD）是一种常见的心理健康状况，但由于缺乏客观标记物，及时检测和干预面临挑战。虽然以往的研究集中在结构化活动中的行为和非言语标志上（如演讲或面试），但这些设置无法完全再现真实世界中无序的社会互动。在自然、未被摆布的环境中识别非言语标志对于开发普遍且非侵入性的监测解决方案至关重要。为此，我们提出了 AnxietyFaceTrack 研究，该研究利用面部视频分析来检测无结构社交环境中的焦虑情况。本项研究包括91名参与者参与与陌生人的社会活动，并使用低成本智能手机摄像头记录了他们的面部视频。我们检查了面部特征，包括眼睛运动、头部位置、面部地标和面部动作单元，并使用自我报告调查数据建立了多分类（焦虑、中立、非焦虑）和二元分类（例如，焦虑 vs 中立）的基准。 我们的结果显示，在多分类识别任务中，基于前20%特征训练的随机森林分类器达到了最高的91.0%准确率；而在二元分类任务中，平均准确率为92.33%。值得注意的是，头部位置和面部地标在各自单独的脸部区域表现最佳：多类分类中的准确性分别为85.0%和88.0%，而二元分类中的准确性分别为89.66%和91.0%。 本研究引入了一种非侵入性且成本效益高的解决方案，可以无缝集成到日常使用的智能手机中，用于持续焦虑监测。这为早期发现和干预提供了一个有前景的途径。
URL
https://arxiv.org/abs/2502.16106
