Clustering-Based Support Vector Machine (SVM) for Symptomatic Knee Osteoarthritis Severity Classification

Halim, Husnir Nasyuha Abdul; Azaman, Aizreena

doi:10.1145/3574198.3574220

Cited by 2 publications

(1 citation statement)

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“…By implementing Support Vector Machine (SVM) with this approach. In [5], the authors incorporated pain scores and gait features as key factors in characterizing KOA. They utilized spatialtemporal, kinematic, and electromyographic correlated features as relevant characteristics.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Improving Knee Osteoarthritis Classification with Markerless Pose Estimation and STGCN Model

Khessiba,

Blaiech,

Abdallah

et al. 2023

2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP)

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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.

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Section: Introduction and Related Workmentioning

confidence: 99%