Instant gait classification for hip osteoarthritis patients: a non-wearable sensor approach utilizing Pearson correlation, SMAPE, and GMM

초록

This study aims to establish a methodology for classifying gait patterns in patients with hip osteoarthritis without the useof wearable sensors. Although patients with the same pathological condition may exhibit significantly different gait patterns,an accurate and efficient classification system is needed: one that reduces the effort and preparation time for bothpatients and clinicians, allowing gait analysis and classification without the need for cumbersome sensors like EMG orcamera-based systems. The proposed methodology follows three key steps. First, ground reaction forces are measured inthree directions-anterior–posterior, medial–lateral, and vertical-using a force plate during gait analysis. These force dataare then evaluated through two approaches: trend similarity is assessed using the Pearson correlation coefficient, whilescale similarity is measured with the Symmetric Mean Absolute Percentage Error (SMAPE), comparing results withhealthy controls. Finally, Gaussian Mixture Models (GMM) are applied to cluster both healthy controls and patients,grouping the patients into distinct categories based on six quantified metrics derived from the correlation and SMAPE. Using the proposed methodology, 16 patients with hip osteoarthritis were successfully categorized into two distinct gaitgroups (Group 1 and Group 2). The gait patterns of these groups were further analyzed by comparing joint momentsand angles in the lower limbs among healthy individuals and the classified patient groups. This study demonstrates thatgait pattern classification can be reliably achieved using only force-plate data, offering a practical tool for personalizedrehabilitation in hip osteoarthritis patients. By incorporating quantitative variables that capture both gait trends and scale,the methodology efficiently classifies patients with just 2–3 ms of natural walking. This minimizes the burden on patientswhile delivering a more accurate and realistic assessment. The proposed approach maintains a level of accuracy comparableto more complex methods, while being easier to implement and more accessible in clinical settings.

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