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arXiv · 2501.05461

Beyond Questionnaires: Video Analysis for Social Anxiety Detection

Nilesh Kumar Sahu·Nandigramam Sai Harshit·Rishabh Uikey·Haroon R. Lone
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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.

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Nilesh Kumar Sahu, Nandigramam Sai Harshit, Rishabh Uikey, Haroon R. Lone. 2024-12-26. Beyond Questionnaires: Video Analysis for Social Anxiety Detection. https://arxiv.org/abs/2501.05461

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