Leveraging machine learning for predicting human body model response in restraint design simulations. Issue 6 (26th April 2021)

• Record Type: Journal Article
• Authors: Joodaki, Hamed; Gepner, Bronislaw; Kerrigan, Jason
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 24:Issue 6(2021)
• Page Start: 597
• Page End: 611
• Publication Date: 2021-04-26
• Subjects: Metamodel -- machine learning -- restraint system optimization; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2020.1841754
• Languages: English
• ISSNs: 1025-5842

Abstract

Abstract: The objective of this study was to leverage and compare multiple machine learning techniques for predicting the human body model response in restraint design simulations. Parametric simulations with 16 independent variables were performed. Ordinary least-squares (OLS), least absolute shrinkage and selection operator (LASSO), neural network (NN), support vector regression (SVR), regression forest (RF), and an ensemble method were used to develop response surface models of the simulations. The hyperparameters of the machine learning techniques were optimized through grid search and cross-validation to avoid under-fitting and over-fitting. The ensemble method outperformed other techniques, followed by LASSO, SVR, NN, RF, and OLS. Findings indicated that optimizing the metamodel hyper-parameters are essential to predict the optimum set of restraint design parameters.