On the prediction of tibiofemoral contact forces for healthy individuals and osteoarthritis patients during gait: a comparative study of regression methods.

Autor: Moura FA; Laboratory of Applied Biomechanics, Sport Sciences Department, State University of Londrina, Londrina, Brazil. felipemoura@uel.br.; Wageningen Data Competence Center, Wageningen University and Research, Wageningen, The Netherlands. felipemoura@uel.br., Pelegrinelli ARM; Laboratory of Applied Biomechanics, Sport Sciences Department, State University of Londrina, Londrina, Brazil.; Human Movement Biomechanics Laboratory, University of Ottawa, Ottawa, Canada., Catelli DS; Human Movement Biomechanics Laboratory, University of Ottawa, Ottawa, Canada.; Department of Movement Sciences, Faculty of Movement and Rehabilitation Sciences, KU Leuven, Leuven, Belgium., Kowalski E; Human Movement Biomechanics Laboratory, University of Ottawa, Ottawa, Canada., Lamontagne M; Human Movement Biomechanics Laboratory, University of Ottawa, Ottawa, Canada., da Silva Torres R; Wageningen Data Competence Center, Wageningen University and Research, Wageningen, The Netherlands. ricardo.torres@ntnu.no.; Department of ICT and Natural Sciences, NTNU-Norwegian University of Science and Technology, Ålesund, Norway. ricardo.torres@ntnu.no.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Jan 16; Vol. 14 (1), pp. 1379. Date of Electronic Publication: 2024 Jan 16.
DOI: 10.1038/s41598-023-50481-x
Abstrakt: Knee osteoarthritis (OA) is a public health problem affecting millions of people worldwide. The intensity of the tibiofemoral contact forces is related to cartilage degeneration, and so is the importance of quantifying joint loads during daily activities. Although simulation with musculoskeletal models has been used to calculate joint loads, it demands high-cost equipment and a very time-consuming process. This study aimed to evaluate consolidated machine learning algorithms to predict tibiofemoral forces during gait analysis of healthy individuals and knee OA patients. Also, we evaluated three different datasets to train each model, considering different combinations of primary kinematic and kinetic data, and post-processing data. We evaluated 14 patients with severe unilateral knee OA and 14 healthy individuals during 3-5 gait trials. Data were split into 70% and 30% of the samples as training and test data. Test data was independently evaluated considering a mixture of pathological and healthy individuals, and only OA and Control patients. The main results showed that accurate predictions of the tibiofemoral contact forces were achieved using machine learning methods and that the predictions were sensitive to changes in the input data as training. The present study provided insights into the most promising regressions methods to predict knee contact forces representing an important starting point for the broader application of biomechanical analysis in clinical environments.
(© 2024. The Author(s).)
Databáze: MEDLINE
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