Improving Knee Osteoarthritis Classification with Markerless Pose Estimation and STGCN Model - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Improving Knee Osteoarthritis Classification with Markerless Pose Estimation and STGCN Model

Résumé

Knee osteoarthritis (KOA) is a debilitating disease that greatly impacts the quality of life, particularly among the elderly population. Conventional subjective assessment methods for KOA have limitations in terms of accuracy and objective diagnosis. This paper proposes an innovative approach by integrating advanced technologies, specifically the Spatio-Temporal Graph Convolutional Network (STGCN), applied to gait analysis from markerless videos, for precise and quantitative assessment of KOA. The STGCN network is applied to normalized data obtained from Blazepose, a markerless pose estimation technique. Evaluated on an academic dataset of 80 RGB videos, it provides an accuracy of 93.75%. By leveraging the capabilities of the STGCN network, this study significantly enhances the classification of KOA based on gait patterns, offering promising prospects for improved diagnosis and treatment strategies for individuals with KOA.
Fichier principal
Vignette du fichier
mmsp23.pdf (429.53 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04336153 , version 1 (11-12-2023)

Identifiants

  • HAL Id : hal-04336153 , version 1

Citer

Souhir Khessiba, Ahmed Ghazi Blaiech, Asma Ben Abdallah, Rim Grassa, Antoine Manzanera, et al.. Improving Knee Osteoarthritis Classification with Markerless Pose Estimation and STGCN Model. International Workshop on Multimedia Signal Processing (IEEE MMSP 2023), Sep 2023, Poitiers, France. ⟨hal-04336153⟩
14 Consultations
40 Téléchargements

Partager

Gmail Facebook X LinkedIn More