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Applying Machine Learning to Gait Analysis Data for Hip Osteoarthritis Diagnosis
Anna Ghidotti1, Daniele Regazzoni1, Caterina Rizzi1
1University of Bergamo, Bergamo, Italy.
Background:
Hip osteoarthritis (OA) is a degenerative joint disease that affects approximately 25% of individuals over their lifetime, with prevalence expected to rise due to population aging. Gait analysis is recognized as a valuable tool for understanding the biomechanical impact of OA, providing insights into how patients adapt to pain and dysfunction.
Objectives:
This study aims to investigate gait differences between hip OA patients and healthy controls using markerless systems and machine learning techniques to classify patients based on gait data.
Methods:
Thirty-six hip OA patients and thirteen healthy controls were analyzed, and machine learning models were trained using gait parameters.
Results:
The Support Vector Classifier model achieved the highest classification F1-score (96%), demonstrating the potential of combining gait analysis with machine learning to support hip OA diagnosis.
Conclusion:
The findings of the present study suggest that machine learning applied to gait analysis data could enhance diagnosis and management of OA in clinical practice.
