An automated method to morph finite element whole-body human models with a wide range of stature and body shape for both men and women. (26th July 2017)

• Record Type: Journal Article
• Authors: Zhang, Kai; Cao, Libo; Fanta, Abeselom; Reed, Matthew P.; Neal, Mark; Wang, Jenne-Tai; Lin, Chin-Hsu; Hu, Jingwen
• Is Part Of: Journal of biomechanics. Volume 60(2017)
• Page Start: 253
• Page End: 260
• Publication Date: 2017-07-26
• Subjects: Mesh morphing -- Human model -- Finite element model -- Chest impact -- Vulnerable population; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2017.06.015
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: Field data analyses have shown that small female, obese, and/or older occupants are at increased risks of death and serious injury in motor-vehicle crashes compared with mid-size young men. The current adult finite element (FE) human models represent occupants in the same three body sizes (large male, mid-size male, and small female) as those for the contemporary adult crash dummies. Further, the time needed to develop an FE human model using the traditional method is measured in months or even years. In the current study, an improved regional mesh morphing method based on landmark-based radial basis function (RBF) interpolation was developed to rapidly morph a mid-size male FE human model into different geometry targets. A total of 100 human models with a wide range of human attributes were generated. A pendulum chest impact condition was applied to each model as an initial assessment of the resulting variability in response. The morphed models demonstrated mesh quality similar to the baseline model. The peak impact forces and chest deflections in the chest pendulum impacts varied substantially with different models, supportive of consideration of population variation in evaluating the occupant injury risks. The method developed in this study will enable future safety design optimizations targeting at various vulnerable populations that cannot be considered with the current models.