Development of a 3D Finite Element Model of the Human Body

Lizee, Emmanuel; Robin, Stéphane; Song, Eric; Bertholon, Nicolas; Coz, Jean-Yves Le; Besnault, Benoît; Lavaste, F.

doi:10.4271/983152

Cited by 62 publications

(41 citation statements)

References 35 publications

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“…The available properties thus cannot be used to represent the human muscle in pedestrian impacts. Existing Finite Element Human body models use a three-element linear viscoelastic material model to model the muscle tissues (Bandak et al 2001;Lizee et al 1998;Chawla et al 2004). The suitability of other material models like Prony series based models and QLV theory model to impact loads is not yet established in the literature.…”

Section: Introductionmentioning

confidence: 99%

Characterization of human passive muscles for impact loads using genetic algorithm and inverse finite element methods

Chawla

Mukherjee

Karthikeyan

2008

Biomech Model Mechanobiol

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The objective of this study is to identify the dynamic material properties of human passive muscle tissues for the strain rates relevant to automobile crashes. A novel methodology involving genetic algorithm (GA) and finite element method is implemented to estimate the material parameters by inverse mapping the impact test data. Isolated unconfined impact tests for average strain rates ranging from 136 s(-1) to 262 s(-1) are performed on muscle tissues. Passive muscle tissues are modelled as isotropic, linear and viscoelastic material using three-element Zener model available in PAMCRASH(TM) explicit finite element software. In the GA based identification process, fitness values are calculated by comparing the estimated finite element forces with the measured experimental forces. Linear viscoelastic material parameters (bulk modulus, short term shear modulus and long term shear modulus) are thus identified at strain rates 136 s(-1), 183 s(-1) and 262 s(-1) for modelling muscles. Extracted optimal parameters from this study are comparable with reported parameters in literature. Bulk modulus and short term shear modulus are found to be more influential in predicting the stress-strain response than long term shear modulus for the considered strain rates. Variations within the set of parameters identified at different strain rates indicate the need for new or improved material model, which is capable of capturing the strain rate dependency of passive muscle response with single set of material parameters for wide range of strain rates.

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Section: Introductionmentioning

confidence: 99%

Characterization of human passive muscles for impact loads using genetic algorithm and inverse finite element methods

Chawla

Mukherjee

Karthikeyan

2008

Biomech Model Mechanobiol

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“…Current finite element (FE) models are limited to certain ages and sexes in the population. The full spectrum of ages was not investigated in any previous studies, and females in particular are underrepresented (Deng et al 1999;El-Jawahri 2010;Gayzik et al 2006;Ito et al 2009;Kent et al 2005;Kimpara et al 2005;Lizee et al 1998;Mattrey et al 2008;Plank 1998;Ruan et al 2003;Song et al 2009;Tamura et al 2005;Vezin and Verriest 2005;Wang and Yang 1998). Though in theory, FE models can represent all ages and sexes, a limitation of FE modeling is the time-consuming process to develop subject-specific meshes.…”

Section: Introductionmentioning

confidence: 99%

Age- and Sex-Specific Thorax Finite Element Model Development and Simulation

Schoell

Weaver

Vavalle

et al. 2015

Traffic Injury Prevention

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Objective:The shape, size, bone density, and cortical thickness of the thoracic skeleton vary significantly with age and sex, which can affect the injury tolerance, especially in at-risk populations such as the elderly. Computational modeling has emerged as a powerful and versatile tool to assess injury risk. However, current computational models only represent certain ages and sexes in the population. The purpose of this study was to morph an existing finite element (FE) model of the thorax to depict thorax morphology for males and females of ages 30 and 70 years old (YO) and to investigate the effect on injury risk.Methods: Age-and sex-specific FE models were developed using thin-plate spline interpolation. In order to execute the thin-plate spline interpolation, homologous landmarks on the reference, target, and FE model are required. An image segmentation and registration algorithm was used to collect homologous rib and sternum landmark data from males and females aged 0-100 years. The Generalized Procrustes Analysis was applied to the homologous landmark data to quantify age-and sex-specific isolated shape changes in the thorax. The Global Human Body Models Consortium (GHBMC) 50th percentile male occupant model was morphed to create age-and sex-specific thoracic shape change models (scaled to a 50th percentile male size). To evaluate the thoracic response, 2 loading cases (frontal hub impact and lateral impact) were simulated to assess the importance of geometric and material property changes with age and sex.Results: Due to the geometric and material property changes with age and sex, there were observed differences in the response of the thorax in both the frontal and lateral impacts. Material property changes alone had little to no effect on the maximum thoracic force or the maximum percent compression. With age, the thorax becomes stiffer due to superior rotation of the ribs, which can result in increased bone strain that can increase the risk of fracture. For the 70-YO models, the simulations predicted a higher number of rib fractures in comparison to the 30-YO models. The male models experienced more superior rotation of the ribs in comparison to the female models, which resulted in a higher number of rib fractures for the males.Conclusion: In this study, age-and sex-specific thoracic models were developed and the biomechanical response was studied using frontal and lateral impact simulations. The development of these age-and sex-specific FE models of the thorax will lead to an improved understanding of the complex relationship between thoracic geometry, age, sex, and injury risk.

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“…Physical properties of the different tissues in the body were based on literature data [21][22][23][24][25][26][27][28][29], and specific experiments were conducted for the HUMOS project. In the model, trabecular and cortical bones were assumed as elastoplastic materials.…”

Section: '[Insert Figure 1]'mentioning

confidence: 99%