用视频分析人体动作和眼神，自动识别社交焦虑症。
Beyond Questionnaires: Video Analysis for Social Anxiety Detection
- 从视频中提取头、身体、眼神和面部微表情特征
- 在92人样本上实现74%的分类准确率
- 适合心理健康筛查与远程干预场景
社交焦虑障碍（SAD）严重影响个人日常生活与人际关系。传统检测依赖面诊和自填问卷，存在耗时长、主观偏差等问题。本文提出一种基于视频分析的SAD早期检测方法，通过分析92名参与者在受控环境下即兴演讲视频中的头部、身体姿态、眼动轨迹及面部动作单元变化。采用多种机器学习与深度学习算法，实现了最高74%的分类准确率。该方法无需侵入性操作，可实时部署，具备良好的可扩展性，有望提升SAD的早期发现与干预能力。
原文摘要 · Abstract (English)
Social Anxiety Disorder (SAD) significantly impacts individuals' daily lives and relationships. The conventional methods for SAD detection involve physical consultations and self-reported questionnaires, but they have limitations such as time consumption and bias. This paper introduces video analysis as a promising method for early SAD detection. Specifically, we present a new approach for detecting SAD in individuals from various bodily features extracted from the video data. We conducted a study to collect video data of 92 participants performing impromptu speech in a controlled environment. Using the video data, we studied the behavioral change in participants' head, body, eye gaze, and action units. By applying a range of machine learning and deep learning algorithms, we achieved an accuracy rate of up to 74\% in classifying participants as SAD or non-SAD. Video-based SAD detection offers a non-intrusive and scalable approach that can be deployed in real-time, potentially enhancing early detection and intervention capabilities.
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