Title: Beyond Questionnaires: Video Analysis for Social Anxiety Detection Show Chinese title

Title: 无需问卷调查：视频分析用于社交焦虑检测

Abstract: 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. Show Chinese abstract

Abstract: 社交焦虑障碍（SAD）对个体的日常生活和人际关系有显著影响。 传统的SAD检测方法涉及身体检查和自我报告问卷，但它们存在耗时和偏差等局限性。 本文介绍了视频分析作为一种有前景的早期SAD检测方法。 具体而言，我们提出了一种新方法，从视频数据中提取的各种身体特征来检测个体的SAD。 我们进行了一项研究，收集了92名参与者在受控环境中即兴演讲的视频数据。 利用这些视频数据，我们研究了参与者头部、身体、眼神注视和动作单元的行为变化。 通过应用一系列机器学习和深度学习算法，我们在将参与者分类为SAD或非SAD方面达到了最高74%的准确率。 基于视频的SAD检测提供了一种非侵入性和可扩展的方法，可以在实时环境中部署，有望提高早期检测和干预能力。