title: Application of neural networks for the prediction of cartilage stress in a musculoskeletal system.
abstract: Traditional finite element (FE) analysis is computationally demanding. The computational time becomes prohibitively long when multiple loading and boundary conditions need to be considered such as in musculoskeletal movement simulations involving multiple joints and muscles. Presented in this study is an innovative approach that takes advantage of the computational efficiency of both the dynamic multibody (MB) method and neural network (NN) analysis. A NN model that captures the behavior of musculoskeletal tissue subjected to known loading situations is built, trained, and validated based on both MB and FE simulation data. It is found that nonlinear, dynamic NNs yield better predictions over their linear, static counterparts. The developed NN model is then capable of predicting stress values at regions of interest within the musculoskeletal system in only a fraction of the time required by FE simulation.
authors.affiliation: Civil and Mechanical Engineering, University of Missouri - Kansas City, Kansas City, MO, USA.
authors.forename: Yunkai
authors.initials: Y
authors.lastname: Lu
authors.forename: Palgun Reddy
authors.initials: PR
authors.lastname: Pulasani
authors.forename: Reza
authors.initials: R
authors.lastname: Derakhshani
authors.forename: Trent M
authors.initials: TM
authors.lastname: Guess
keywords: Cartilage stress; Finite element analysis; Musculoskeletal simulation; Neural networks