Beyond Questionnaires: Video Analysis for Social Anxiety Detection
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916559592620032 |
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| author | Sahu, Nilesh Kumar Harshit, Nandigramam Sai Uikey, Rishabh Lone, Haroon R. |
| author_facet | Sahu, Nilesh Kumar Harshit, Nandigramam Sai Uikey, Rishabh Lone, Haroon R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05461 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Beyond Questionnaires: Video Analysis for Social Anxiety Detection Sahu, Nilesh Kumar Harshit, Nandigramam Sai Uikey, Rishabh Lone, Haroon R. Computers and Society Computer Vision and Pattern Recognition Human-Computer Interaction 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. |
| title | Beyond Questionnaires: Video Analysis for Social Anxiety Detection |
| topic | Computers and Society Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.05461 |