Measuring Anxiety Levels with Head Motion Patterns in Severe Depression Population
Journal:
arXiv
Published Date:
Feb 12, 2025
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.
