An investigation of human body model morphing for the assessment of abdomen responses to impact against a population of test subjects

Abstract

Objective: Human body models have the potential to better describe the human anatomy and variability than dummies. However, data sets available to verify the human response to impact are typically limited in numbers, and they are not size or gender specific. The objective of this study was to investigate the use of model morphing methodologies within that context. Methods: In this study, a simple human model scaling methodology was developed to morph two detailed human models (Global Human Body Model Consortium models 50th male, M50, and 5th female, F05) to the dimensions of post mortem human surrogates (PMHS) used in published literature. The methodology was then successfully applied to 52 PMHS tested in 14 impact conditions loading the abdomen. The corresponding 104 simulations were compared to the responses of the PMHS and to the responses of the baseline models without scaling (28 simulations). The responses were analysed using the CORA method and peak values. Results:The results suggest that model scaling leads to an improvement of the predicted force and deflection but has more marginal effects on the predicted abdominal compressions. M50 and F05 models scaled to the same PMHS were also found to have similar external responses, but large differences were found between the two sets of models for the strain energy densities in the liver and the spleen for mid-abdomen impact simulations. These differences, which were attributed to the anatomical differences in the abdomen of the baseline models, highlight the importance of the selection of the impact condition for simulation studies, especially if the organ location is not known in the test. Conclusions: While the methodology could be further improved, it shows the feasibility of using model scaling methodologies to compare human models of different sizes and to evaluate scaling approaches within the context of human model validation.

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

“…Another technique for efficient generation of subject-specific models is by mesh morphing (also called warping). The concept has been used extensively in many biomechanics fields on different organs (Couteau et al 2000;Castellano-Smith et al 2001;Fernandez et al 2004;Sigal et al 2008;Bucki et al 2010;Bijar et al 2016;Park et al 2017), fullbody models (Davis et al 2016;Beillas and Berthet 2017;Liu et al 2020), as well as smooth brain models (Hu et al 2007;Ji et al 2011Ji et al , 2015Wu et al 2019), showing promising results. A typical procedure includes image registration (rigid/affine and/or followed by nonlinear registration algorithms), from which displacement field representing the geometrical difference between the subject and baseline mesh is obtained.…”

Occupant Safety in Automated Vehicles

“…This is likely due to a combination of the stiffness of the spine, and the stiffness of the superficial tissues. Future work should include examining advanced positioning and morphing tools, such as PIPER (2,3) to evaluate their potential utility in positioning in reclined postures.…”

An anatomically accurate and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

“…Another technique for efficient generation of subject-specific models is by mesh morphing (also called warping). The concept has been used extensively in many biomechanics fields on different organs (Couteau et al 2000;Castellano-Smith et al 2001;Fernandez et al 2004;Sigal et al 2008;Bucki et al 2010;Bijar et al 2016;Park et al 2017), full-body models (Davis et al 2016;Beillas and Berthet 2017;Liu et al 2020), as well as smooth brain models (Hu et al 2007;Ji et al 2011;Ji et al 2015;Wu et al 2019), showing promising results. A typical procedure includes image registration (rigid/affine and/or followed by nonlinear registration algorithms), from which displacement field representing the geometrical difference between the subject and baseline mesh is obtained.…”