Your browser doesn't support javascript.
loading
On the prediction of tibiofemoral contact forces for healthy individuals and osteoarthritis patients during gait: a comparative study of regression methods.
Moura, Felipe Arruda; Pelegrinelli, Alexandre R M; Catelli, Danilo S; Kowalski, Erik; Lamontagne, Mario; da Silva Torres, Ricardo.
Afiliação
  • Moura FA; Laboratory of Applied Biomechanics, Sport Sciences Department, State University of Londrina, Londrina, Brazil. felipemoura@uel.br.
  • Pelegrinelli ARM; Wageningen Data Competence Center, Wageningen University and Research, Wageningen, The Netherlands. felipemoura@uel.br.
  • Catelli DS; Laboratory of Applied Biomechanics, Sport Sciences Department, State University of Londrina, Londrina, Brazil.
  • 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; Department of Movement Sciences, Faculty of Movement and Rehabilitation Sciences, KU Leuven, Leuven, Belgium.
Sci Rep ; 14(1): 1379, 2024 01 16.
Article em En | MEDLINE | ID: mdl-38228640
ABSTRACT
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.
Assuntos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Osteoartrite do Joelho / Articulação Tibiofemoral Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Reino Unido
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Osteoartrite do Joelho / Articulação Tibiofemoral Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Reino Unido