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Osteoarthritis (OA) is the most common joint disease and a leading cause of disability, most commonly affecting the knee. Conventional diagnostics rely primarily on imaging, which often detects changes only in advanced stages. This pilot study explores an alternative approach - encoder-based motion analysis combined with machine learning - to support early functional assessment of knee OA. The study included 90 subjects: 45 patients with radiographic evidence of OA and 45 healthy controls. A high-resolution rotary encoder integrated into a stabilizing knee orthosis recorded joint flexion-extension angles and velocities during open kinetic chain (OKC) and closed kinetic chain (CKC) tasks. Each subject performed five repetitions for each condition. Statistical analyses (Mann-Whitney U-test) revealed significant differences between groups, particularly in the CKC condition, where OA patients consistently required more time to complete movements. Machine learning classifiers were trained on cycle duration features. For OKC, accuracy remained modest (Naive Bayes: 65.6%), whereas CKC-based features provided stronger discrimination, with a narrow neural network achieving 80% accuracy and balanced sensitivity/specificity. The results demonstrate the feasibility of wearable encoder-based systems for objective, non-invasive assessment of knee function. CKC tasks showed higher diagnostic value, highlighting their potential for integration into clinical protocols. Future research should expand data sets, incorporate multimodal sensors, and use advanced algorithms to improve diagnostic performance and support real-world monitoring.
Karpiński et al. (Wed,) studied this question.