2011
DOI: 10.1016/j.bspc.2010.08.005
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Nonlinear surrogate modeling of tibio-femoral joint interactions

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Cited by 8 publications
(5 citation statements)
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“…1) is built in both the FE and MB framework to emulate the typical contact interaction between the femur cartilage and tibia cartilage [1,68,18]. The model includes a tibia plateau with a contour derived from medical images of a human subject and a semispherical femoral condyle with a radius of 20 mm.…”
Section: Model Development and Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…1) is built in both the FE and MB framework to emulate the typical contact interaction between the femur cartilage and tibia cartilage [1,68,18]. The model includes a tibia plateau with a contour derived from medical images of a human subject and a semispherical femoral condyle with a radius of 20 mm.…”
Section: Model Development and Methodsmentioning
confidence: 99%
“…Artificial neural networks (NN) have been successfully used in various biomechanical modeling scenarios [18]. NNs are universal function approximators that use supervised learning to create the desired nonlinear input–output relationship (black-box modeling).…”
Section: Introductionmentioning
confidence: 99%
“…In DF test (Dichey & Fuller, 1979) (Assaf, 2008). Amongst numerous nonlinear tests, surrogate data methods are popularly used (Mishra et al, 2011). In surrogate data methods, the first step is to generate surrogate data both with linear characteristic as null hypothesis assumes and with main properties of the original data.…”
Section: Data Characteristics Testingmentioning
confidence: 99%
“…As far as nonlinear test, surrogate data based method is employed in this study. In the method, Fourier transform (FT) is introduced, and the surrogating data are generated by multiplying the FT of the data by random phases and then transforming back to the time domain (Mishra et al, 2011). Then, the distributions of the surrogate data and the original data are compared via Kolmogorov-Smirnoff (KS) tests (Unsworth, 2001).…”
Section: Data Characteristics Testingmentioning
confidence: 99%
“…Kriging-based contact models have been used in an optimization approach that predicted muscle forces, tibiofemoral contact forces, and patellofemoral contact forces simultaneously in the knee during walking [14]. Other efforts to create surrogate knee contact models include a Hammerstein–Wiener model, a nonlinear autoregressive model with exogenous input, and a time delay artificial neural network [15]. In addition, a surrogate foot-ground contact model has been created using a lazy learning interpolation method [16].…”
Section: Introductionmentioning
confidence: 99%