{"title":"Comparison of Kriging and Moving Least Square Methods to Change the Geometry of Human Body Models.","authors":"E. Jolivet, Y. Lafon, P. Petit, P. Beillas","doi":"10.4271/2015-22-0013","DOIUrl":null,"url":null,"abstract":"Finite Element Human Body Models (HBM) have become powerful tools to study the response to impact. However, they are typically only developed for a limited number of sizes and ages. Various approaches driven by control points have been reported in the literature for the non-linear scaling of these HBM into models with different geometrical characteristics. The purpose of this study is to compare the performances of commonly used control points based interpolation methods in different usage scenarios. Performance metrics include the respect of target, the mesh quality and the runability. For this study, the Kriging and Moving Least square interpolation approaches were compared in three test cases. The first two cases correspond to changes of anthropometric dimensions of (1) a child model (from 6 to 1.5 years old) and (2) the GHBMC M50 model (Global Human Body Models Consortium, from 50th to 5th percentile female). For the third case, the GHBMC M50 ribcage was scaled to match the rib cage geometry derived from a CT-scan. In the first two test cases, all tested methods provided similar shapes with acceptable results in terms of time needed for the deformation (a few minutes at most), overall respect of the targets, element quality distribution and time step for explicit simulation. The personalization of rib cage proved to be much more challenging. None of the methods tested provided fully satisfactory results at the level of the rib trajectory and section. There were corrugated local deformations unless using a smooth regression through relaxation. Overall, the results highlight the importance of the target definition over the interpolation method.","PeriodicalId":35289,"journal":{"name":"Stapp car crash journal","volume":"59 1","pages":"337-57"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stapp car crash journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4271/2015-22-0013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 27
Abstract
Finite Element Human Body Models (HBM) have become powerful tools to study the response to impact. However, they are typically only developed for a limited number of sizes and ages. Various approaches driven by control points have been reported in the literature for the non-linear scaling of these HBM into models with different geometrical characteristics. The purpose of this study is to compare the performances of commonly used control points based interpolation methods in different usage scenarios. Performance metrics include the respect of target, the mesh quality and the runability. For this study, the Kriging and Moving Least square interpolation approaches were compared in three test cases. The first two cases correspond to changes of anthropometric dimensions of (1) a child model (from 6 to 1.5 years old) and (2) the GHBMC M50 model (Global Human Body Models Consortium, from 50th to 5th percentile female). For the third case, the GHBMC M50 ribcage was scaled to match the rib cage geometry derived from a CT-scan. In the first two test cases, all tested methods provided similar shapes with acceptable results in terms of time needed for the deformation (a few minutes at most), overall respect of the targets, element quality distribution and time step for explicit simulation. The personalization of rib cage proved to be much more challenging. None of the methods tested provided fully satisfactory results at the level of the rib trajectory and section. There were corrugated local deformations unless using a smooth regression through relaxation. Overall, the results highlight the importance of the target definition over the interpolation method.