Titre :

Measuring Anxiety Levels with Head Motion Patterns in Severe Depression Population

Auteur(s) :

Boualeb, Fouad [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Pierson, Emery [Auteur]
Laboratoire d'informatique de l'École polytechnique [Palaiseau] [LIX]
Doudeau, Nicolas [Auteur]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Nineuil, Clémence [Auteur]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Amad, Ali [Auteur]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Daoudi, Mohamed [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la manifestation scientifique :

19th IEEE International Conference on Automatic Face and Gesture Recognition

Ville :

Clearwater (Florida)

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2025-05-26

Discipline(s) HAL :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Sciences cognitives/Neurosciences

Résumé en anglais : [en]

<div><p>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 ...
Lire la suite ><div><p>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 displacementduring 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.</p></div>Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

Date de dépôt :

2025-02-15T03:06:07Z