Year: 2025
Type: book-chapter
Study design: Observational Study
Abstract: 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.
DOI: 10.3233/shti250178
Institutions: University of Bergamo, Humanitas Gavazzeni
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409774216