Looking At The Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress

TL;DR

This paper introduces a new dataset and a multi-modal deep learning approach to automatically detect psychological distress through body gestures, self-adaptors, and audio-visual cues, advancing research in non-verbal distress analysis.

Contribution

It presents a novel dataset of full body videos with distress labels and a new method for detecting self-adaptors and fidgeting, improving automatic distress prediction.

Findings

The proposed model accurately predicts distress levels using combined audio-visual and behavioral features.

Automatic detection of fidgeting correlates with psychological distress.

Multi-modal deep learning outperforms single-modality approaches.

Abstract

Psychological distress is a significant and growing issue in society. Automatic detection, assessment, and analysis of such distress is an active area of research. Compared to modalities such as face, head, and vocal, research investigating the use of the body modality for these tasks is relatively sparse. This is, in part, due to the limited available datasets and difficulty in automatically extracting useful body features. Recent advances in pose estimation and deep learning have enabled new approaches to this modality and domain. To enable this research, we have collected and analyzed a new dataset containing full body videos for short interviews and self-reported distress labels. We propose a novel method to automatically detect self-adaptors and fidgeting, a subset of self-adaptors that has been shown to be correlated with psychological distress. We perform analysis on statistical body gestures and fidgeting features to explore how distress levels affect participants' behaviors. We then propose a multi-modal approach that combines different feature representations using Multi-modal Deep Denoising Auto-Encoders and Improved Fisher Vector Encoding. We demonstrate that our proposed model, combining audio-visual features with automatically detected fidgeting behavioral cues, can successfully predict distress levels in a dataset labeled with self-reported anxiety and depression levels.