Measuring Anxiety Levels with Head Motion Patterns in Severe Depression Population

TL;DR

This study introduces a noninvasive method to quantify anxiety severity in severely depressed patients by analyzing head motion patterns during video interviews, achieving high prediction accuracy.

Contribution

The paper presents a novel approach using head motion analysis to predict anxiety levels in depression, leveraging a new dataset and regression techniques.

Findings

Achieved a mean absolute error of 0.35 in predicting anxiety severity.

Head motion features significantly correlate with anxiety levels.

Method enhances understanding and assessment of anxiety in depression.

Abstract

Depression and anxiety are prevalent mental health disorders that frequently cooccur, with anxiety significantly influencing both the manifestation and treatment of depression. An accurate assessment of anxiety levels in individuals with depression is crucial to develop effective and personalized treatment plans. This study proposes a new noninvasive method for quantifying anxiety severity by analyzing head movements -- specifically speed, acceleration, and angular displacement -- during video-recorded interviews with patients suffering from severe depression. Using data from a new CALYPSO Depression Dataset, we extracted head motion characteristics and applied regression analysis to predict clinically evaluated anxiety levels. Our results demonstrate a high level of precision, achieving a mean absolute error (MAE) of 0.35 in predicting the severity of psychological anxiety based on head movement patterns. This indicates that our approach can enhance the understanding of anxiety's role in depression and assist psychiatrists in refining treatment strategies for individuals.