# Beyond Questionnaires: Video Analysis for Social Anxiety Detection

arXiv: https://arxiv.org/abs/2501.05461 · HTML: https://mlsanity.com/paper/2501.05461/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [HCFSLN: Adaptive Hyperbolic Few-Shot Learning for Multimodal Anxiety Detection](https://mlsanity.com/paper/2511.06988/) (2025)

## Abstract (arXiv)

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.