Should anthropometric differences between the commonly used pedestrian computational biomechanics models and Chinese population be taken into account when predicting pedestrian head kinematics and injury in vehicle collisions in China?

Anthropometry Car-to-pedestrian collision Computational biomechanics model Head injury Multibody model Population

Journal

Accident; analysis and prevention
ISSN: 1879-2057
Titre abrégé: Accid Anal Prev
Pays: England
ID NLM: 1254476

Informations de publication

Date de publication:
Aug 2022
Historique:
received: 23 01 2022
revised: 27 04 2022
accepted: 17 05 2022
pubmed: 1 6 2022
medline: 15 6 2022
entrez: 31 5 2022
Statut: ppublish

Résumé

Computational biomechanics models play a key role in predicting/evaluating pedestrian head kinematics and injury risk in car-to-pedestrian collisions. The human multibody models most commonly used in car-to-pedestrian collision reconstruction, such as pedestrian model by The Netherlands Organisation for Applied Scientific Research TNO, are built using the anthropometry of Western European population as defined in TNO (2013) human multibody model database. In this study, we investigate the effects of the anthropometric differences between the Western European and Chinese populations on the pedestrian head kinematics and injury responses predicted using multibody models. The comparison was conducted through car-to-pedestrian collision simulations using pedestrian multibody models representing anthropometric characteristics of Western European and Chinese populations, three typical vehicle shapes (sedan, SUV and minivan), five initial vehicle impact speeds (30, 35, 40, 45, 50 km/h), and six pedestrian walking postures. The results indicate that the change of pedestrian model anthropometry (from Western European to Chinese) exerts appreciable effects on both the predicted initial boundary conditions of the head-to-windscreen impact (in particular the head-to-windscreen impact angle) and the head injury indices in the impact with the road surface (secondary impact).

Identifiants

pubmed: 35640364
pii: S0001-4575(22)00154-3
doi: 10.1016/j.aap.2022.106718
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

106718

Informations de copyright

Copyright © 2022 Elsevier Ltd. All rights reserved.

Auteurs

Fang Wang (F)

School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China.

Jiajie Yin (J)

School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China.

Lin Hu (L)

School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China. Electronic address: hulin@csust.edu.cn.

Mingliang Wang (M)

School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China.

Xin Liu (X)

School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China.

Karol Miller (K)

Intelligent Systems for Medicine Laboratory, Department of Mechanical Engineering, The University of Western Australia, Perth 6009, Western Australia, Australia; Harvard Medical School, Boston, MA, USA.

Adam Wittek (A)

Intelligent Systems for Medicine Laboratory, Department of Mechanical Engineering, The University of Western Australia, Perth 6009, Western Australia, Australia. Electronic address: adam.wittek@uwa.edu.au.

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