TY - GEN
T1 - Improving Knee Osteoarthritis Classification with Markerless Pose Estimation and STGCN Model
AU - Khessiba, Souhir
AU - Blaiech, Ahmed Ghazi
AU - Abdallah, Asma Ben
AU - Grassa, Rim
AU - Manzanera, Antoine
AU - Bedoui, Mohamed Hedi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
KW - Deep Learning
KW - Gait analysis
KW - KOA classification
KW - Pose estimation
KW - STGCN model
U2 - 10.1109/MMSP59012.2023.10337688
DO - 10.1109/MMSP59012.2023.10337688
M3 - Conference contribution
AN - SCOPUS:85181584851
T3 - 2023 IEEE 25th International Workshop on Multimedia Signal Processing, MMSP 2023
BT - 2023 IEEE 25th International Workshop on Multimedia Signal Processing, MMSP 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE International Workshop on Multimedia Signal Processing, MMSP 2023
Y2 - 27 September 2023 through 29 September 2023
ER -