VAG Signal-Based Computational System for Consumer’s Utilization Devices in Osteoarthritis Data Extraction and Classification

Utilizing IoT devices for automated signal extraction and data processing is a cornerstone of Computer-Aided Diagnostics, addressing various clinical challenges. Among these, diagnosing osteoarthritis, a critical knee joint disorder early on is paramount to prevent severe joint damage. Vibroarthrography (VAG), a novel approach, leverages sound waves produced during knee joint movement to diagnose various stages of this disorder. This article presents a computational system based on VAG signals, seamlessly integrated with IoT devices for knee joint data extraction. Employing machine learning techniques facilitates the classification of osteoarthritis levels. By offering this system as consumer electronics, it reduces costs and radiation exposure compared to traditional clinical modalities. Our implementation gathered 187 clinical data points using the proposed computational system, integrating IoT devices to capture vibrations. Analyzing the recorded data involved computing various feature sets, enabling multiple classifications of osteoarthritis levels. Evaluation based on accuracy, precision, recall, and AUC demonstrated the efficacy of our proposed binary and multiclass classification models, indicating its potential as a mechanism for collecting and analyzing data for early-stage osteoarthritis detection.