Titre :

Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics

Auteur(s) :

Szczapa, Benjamin [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Daoudi, Mohamed [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Berretti, Stefano [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Pala, Pietro [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Bimbo, Alberto [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Hammal, Zakia [Auteur]
Carnegie Mellon University [Pittsburgh] [CMU]

Titre de la manifestation scientifique :

25th International Conference on Pattern Recognition

Ville :

Milano

Pays :

Italie

Date de début de la manifestation scientifique :

2021-01-10

Discipline(s) HAL :

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

Résumé en anglais : [en]

We propose an automatic method for pain intensity measurement from video. For each video, pain intensity was measured using the dynamics of facial movement using 66 facial points. Gram matrices formulation was used for ...
Lire la suite >We propose an automatic method for pain intensity measurement from video. For each video, pain intensity was measured using the dynamics of facial movement using 66 facial points. Gram matrices formulation was used for facial points trajectory representations on the Riemannian manifold of symmetric positive semi-definite matrices of fixed rank. Curve fitting and temporal alignment were then used to smooth the extracted trajectories. A Support Vector Regression model was then trained to encode the extracted trajectories into ten pain intensity levels consistent with the Visual Analogue Scale for pain intensity measurement. The proposed approach was evaluated using the UNBC McMaster Shoulder Pain Archive and was compared to the state-of-the-art on the same data. Using both 5-fold cross-validation and leave-one-subject-out cross-validation, our results are competitive with respect to state-of-the-art methods.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