Citation

Giordano C, Li X, Kleiven S. PLoS One 2017; 12(11): e0187916.

Affiliation

Division of Neuronic Engineering, School of Technology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.

Copyright

(Copyright © 2017, Public Library of Science)

DOI

10.1371/journal.pone.0187916

PMID

29135997

Abstract

Human body models (HBMs) have the potential to provide significant insights into the pediatric response to impact. This study describes a scalable/posable approach to perform child accident reconstructions using the Position and Personalize Advanced Human Body Models for Injury Prediction (PIPER) scalable child HBM of different ages and in different positions obtained by the PIPER tool. Overall, the PIPER scalable child HBM managed reasonably well to predict the injury severity and location of the children involved in real-life crash scenarios documented in the medical records. The developed methodology and workflow is essential for future work to determine child injury tolerances based on the full Child Advanced Safety Project for European Roads (CASPER) accident reconstruction database. With the workflow presented in this study, the open-source PIPER scalable HBM combined with the PIPER tool is also foreseen to have implications for improved safety designs for a better protection of children in traffic accidents.

Language: en