Evaluation of the head protection effectiveness of cyclist helmets using full-scale computational biomechanics modelling of cycling accidents.


Journal

Journal of safety research
ISSN: 1879-1247
Titre abrégé: J Safety Res
Pays: United States
ID NLM: 1264241

Informations de publication

Date de publication:
02 2022
Historique:
received: 06 07 2020
revised: 27 12 2020
accepted: 18 11 2021
entrez: 7 3 2022
pubmed: 8 3 2022
medline: 30 4 2022
Statut: ppublish

Résumé

Cycling is a popular choice for urban transportation. Helmets are important and the most popular means of head protection for cyclists. However, a debate about the effectiveness of helmets in protecting a cyclist's head from injury continues. We employed computational biomechanics methods to analyze the head protection effectiveness of nine off-the-shelf-helmets for two typical impact scenarios that occur in cycling accidents: cyclist's head impacting a kerb (kerb-impact) and cyclist skidding (skidding impact) on the road surface. We conducted drop tests for all nine analyzed helmets, and used the test data for validation of the corresponding helmet finite element (FE) models created in this study. The validated helmet models were then used in the full-scale computer simulations (FE analysis for the skull, brain and helmet, and multibody dynamics for the remaining segments of the cyclist's body) of the cycling accidents for cyclists wearing a helmet and without a helmet. The results indicate that helmets can reduce both the peak linear acceleration of the cyclist head center of gravity (COG) and the risk of cyclist skull fracture. However, higher rotational acceleration of the head COG was predicted for cyclists wearing helmets. The results obtained using the injury criteria that rely on the brain deformations (maximum shear strain MPS and cumulative strain damage measure CSDM) suggest that helmets may offer protection in all the analyzed cyclist impact scenarios. However, the predicted level of protection varies for different helmets and impact scenarios with appreciable variations in the predictions obtained using different injury criteria. Reduction in the maximum principal strain (MPS

Identifiants

pubmed: 35249593
pii: S0022-4375(21)00144-4
doi: 10.1016/j.jsr.2021.11.005
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

109-134

Informations de copyright

Copyright © 2021 National Safety Council and Elsevier Ltd. All rights reserved.

Auteurs

Fang Wang (F)

School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410015, Hunan, China. Electronic address: wangfang@csust.edu.cn.

Junzhi Wu (J)

School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, Fujian, China.

Lin Hu (L)

School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410015, Hunan, China. Electronic address: hulin888@sohu.com.

Chao Yu (C)

School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, Fujian, China.

Bingyu Wang (B)

School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, Fujian, China.

Xiaoqun Huang (X)

School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, Fujian, China.

Karol Miller (K)

Intelligent System for Medicine Laboratory, Department of Mechanical Engineering, The University of Western Australia, Perth 6009, Western Australia, Australia; Harvard Medical School, Boston 02115, MA, USA.

Adam Wittek (A)

Intelligent System for Medicine Laboratory, Department of Mechanical Engineering, The University of Western Australia, Perth 6009, Western Australia, Australia.

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Classifications MeSH