Application of neural networks for the prediction of cartilage stress in a musculoskeletal system |
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Authors: | Yunkai Lu Palgun Reddy Pulasani Reza Derakhshani Trent M Guess |
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Institution: | 1. Civil and Mechanical Engineering, University of Missouri – Kansas City, Kansas City, MO, USA;2. Electrical Engineering, University of Missouri – Kansas City, Kansas City, MO, USA;3. Mechanical Engineering, University of Missouri – Kansas City, 350K Flarsheim Hall, 5110 Rockhill Road, Kansas City, MO, USA 64110 |
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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. |
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Keywords: | Finite element analysis Musculoskeletal simulation Neural networks Cartilage stress |
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