Exploring the Role of Emotion Regulation Difficulties in the Assessment of Mental Disorders

TL;DR

This study investigates how difficulties in emotion regulation can serve as a useful indicator for automatically detecting mental disorders using audio-video data collected during human-computer interactions.

Contribution

It introduces the use of emotion regulation difficulties as an intermediate feature for mental disorder detection from audio-video data.

Findings

ERD can effectively distinguish mental disorder cases from controls.

Audio-video data combined with ERD features improve detection accuracy.

The approach demonstrates potential for non-invasive mental health assessment.

Abstract

Several studies have been reported in the literature for the automatic detection of mental disorders. It is reported that mental disorders are highly correlated. The exploration of this fact for the automatic detection of mental disorders is yet to explore. Emotion regulation difficulties (ERD) characterize several mental disorders. Motivated by that, we investigated the use of ERD for the detection of two opted mental disorders in this study. For this, we have collected audio-video data of human subjects while conversing with a computer agent based on a specific questionnaire. Subsequently, a subject's responses are collected to obtain the ground truths of the audio-video data of that subject. The results indicate that the ERD can be used as an intermediate representation of audio-video data for detecting mental disorders.