Knee Joint Health Care Monitoring System using AI and IoT - Classification Approach

Knee osteoarthritis is a significant global health concern and the third major problem in India. Effective diagnosis and monitoring of knee joint health are crucial, and Vibroarthrography (VAG) has been adopted as a diagnostic procedure. Vibroarthrography (VAG) signal denoising is done by using Variational Mode Decomposition (VMD). The work investigates the effectiveness of VMD in separating a signal into its intrinsic components, followed by the identification and removal of noise-dominated modes. Various time-domain, frequency-domain, and spectral features are extracted from the decomposed modes and the reconstructed signal to gain insights into the signal characteristics. Machine learning classifiers are then utilized to categorize the signals. Our results indicate that ensemble classifiers, specifically the Voting Classifier and AdaBoost, achieved an accuracy of 93% in distinguishing between normal and abnormal VAG signals.