Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents

This study evaluates the effectiveness of various widely used head injury criteria (HICs) in predicting vulnerable road user (VRU) head injuries due to road traffic accidents. Thirty-one real-world car-to-VRU impact accident cases with detailed head injury records were collected and replicated throu...

Full description

Bibliographic Details
Main Authors: Fang Wang, Zhen Wang, Lin Hu, Hongzhen Xu, Chao Yu, Fan Li
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-06-01
Series:Frontiers in Bioengineering and Biotechnology
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fbioe.2021.677982/full
id doaj-dd05e9b887a3498db0b89b97338e2408
record_format Article
spelling doaj-dd05e9b887a3498db0b89b97338e24082021-06-29T05:10:50ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852021-06-01910.3389/fbioe.2021.677982677982Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact AccidentsFang Wang0Zhen Wang1Lin Hu2Hongzhen Xu3Chao Yu4Fan Li5School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, ChinaSchool of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, ChinaSchool of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, ChinaThis study evaluates the effectiveness of various widely used head injury criteria (HICs) in predicting vulnerable road user (VRU) head injuries due to road traffic accidents. Thirty-one real-world car-to-VRU impact accident cases with detailed head injury records were collected and replicated through the computational biomechanics method; head injuries observed in the analyzed accidents were reconstructed by using a finite element (FE)-multibody (MB) coupled pedestrian model [including the Total Human Model for Safety (THUMS) head–neck FE model and the remaining body segments of TNO MB pedestrian model], which was developed and validated in our previous study. Various typical HICs were used to predict head injuries in all accident cases. Pearson’s correlation coefficient analysis method was adopted to investigate the correlation between head kinematics-based injury criteria and the actual head injury of VRU; the effectiveness of brain deformation-based injury criteria in predicting typical brain injuries [such as diffuse axonal injury diffuse axonal injury (DAI) and contusion] was assessed by using head injury risk curves reported in the literature. Results showed that for head kinematics-based injury criteria, the most widely used HICs and head impact power (HIP) can accurately and effectively predict head injury, whereas for brain deformation-based injury criteria, the maximum principal strain (MPS) behaves better than cumulative strain damage measure (CSDM0.15 and CSDM0.25) in predicting the possibility of DAI. In comparison with the dilatation damage measure (DDM), MPS seems to better predict the risk of brain contusion.https://www.frontiersin.org/articles/10.3389/fbioe.2021.677982/fullhead injury criterioninjury predictionvulnerable road userimpact accident reconstructioncomputational biomechanics model
collection DOAJ
language English
format Article
sources DOAJ
author Fang Wang
Zhen Wang
Lin Hu
Hongzhen Xu
Chao Yu
Fan Li
spellingShingle Fang Wang
Zhen Wang
Lin Hu
Hongzhen Xu
Chao Yu
Fan Li
Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents
Frontiers in Bioengineering and Biotechnology
head injury criterion
injury prediction
vulnerable road user
impact accident reconstruction
computational biomechanics model
author_facet Fang Wang
Zhen Wang
Lin Hu
Hongzhen Xu
Chao Yu
Fan Li
author_sort Fang Wang
title Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents
title_short Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents
title_full Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents
title_fullStr Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents
title_full_unstemmed Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents
title_sort evaluation of head injury criteria for injury prediction effectiveness: computational reconstruction of real-world vulnerable road user impact accidents
publisher Frontiers Media S.A.
series Frontiers in Bioengineering and Biotechnology
issn 2296-4185
publishDate 2021-06-01
description This study evaluates the effectiveness of various widely used head injury criteria (HICs) in predicting vulnerable road user (VRU) head injuries due to road traffic accidents. Thirty-one real-world car-to-VRU impact accident cases with detailed head injury records were collected and replicated through the computational biomechanics method; head injuries observed in the analyzed accidents were reconstructed by using a finite element (FE)-multibody (MB) coupled pedestrian model [including the Total Human Model for Safety (THUMS) head–neck FE model and the remaining body segments of TNO MB pedestrian model], which was developed and validated in our previous study. Various typical HICs were used to predict head injuries in all accident cases. Pearson’s correlation coefficient analysis method was adopted to investigate the correlation between head kinematics-based injury criteria and the actual head injury of VRU; the effectiveness of brain deformation-based injury criteria in predicting typical brain injuries [such as diffuse axonal injury diffuse axonal injury (DAI) and contusion] was assessed by using head injury risk curves reported in the literature. Results showed that for head kinematics-based injury criteria, the most widely used HICs and head impact power (HIP) can accurately and effectively predict head injury, whereas for brain deformation-based injury criteria, the maximum principal strain (MPS) behaves better than cumulative strain damage measure (CSDM0.15 and CSDM0.25) in predicting the possibility of DAI. In comparison with the dilatation damage measure (DDM), MPS seems to better predict the risk of brain contusion.
topic head injury criterion
injury prediction
vulnerable road user
impact accident reconstruction
computational biomechanics model
url https://www.frontiersin.org/articles/10.3389/fbioe.2021.677982/full
work_keys_str_mv AT fangwang evaluationofheadinjurycriteriaforinjurypredictioneffectivenesscomputationalreconstructionofrealworldvulnerableroaduserimpactaccidents
AT zhenwang evaluationofheadinjurycriteriaforinjurypredictioneffectivenesscomputationalreconstructionofrealworldvulnerableroaduserimpactaccidents
AT linhu evaluationofheadinjurycriteriaforinjurypredictioneffectivenesscomputationalreconstructionofrealworldvulnerableroaduserimpactaccidents
AT hongzhenxu evaluationofheadinjurycriteriaforinjurypredictioneffectivenesscomputationalreconstructionofrealworldvulnerableroaduserimpactaccidents
AT chaoyu evaluationofheadinjurycriteriaforinjurypredictioneffectivenesscomputationalreconstructionofrealworldvulnerableroaduserimpactaccidents
AT fanli evaluationofheadinjurycriteriaforinjurypredictioneffectivenesscomputationalreconstructionofrealworldvulnerableroaduserimpactaccidents
_version_ 1721355586720759808