Evidence map›Paper›PMID 41644391›Full record

ArticleChinese journal of traumatology = Zhonghua chuang shang za zhi2026

Optimization of chest deflection definition in human body models: Implication for pedestrian injury assessment.

Guojie Wang, Yongcheng Long, Bing Zhang, Yu Xiao, James Cheng, Yi Chang, Yu Liu

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Article in Chinese journal of traumatology = Zhonghua chuang shang za zhi, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Guojie WangChina Automotive Engineering Research Institute Co., Ltd., Chongqing, 401122, China.
Yongcheng LongChina Automotive Engineering Research Institute Co., Ltd., Chongqing, 401122, China.
Bing ZhangChina Automotive Engineering Research Institute Co., Ltd., Chongqing, 401122, China.
Yu XiaoState Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha, 410082, China.
James ChengChina Automotive Engineering Research Institute Co., Ltd., Chongqing, 401122, China.
Yi ChangChina Automotive Engineering Research Institute Co., Ltd., Chongqing, 401122, China.
Yu LiuChina Automotive Engineering Research Institute Co., Ltd., Chongqing, 401122, China. Electronic address: liuyu@caeri.com.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePedestrian chest injuries remain a significant global concern, ranking as the second leading cause of death in traffic accidents. Although human body model (HBM) provides a strain-based methodology for predicting pedestrian chest injuries, conventional deflection-based injury metrics remain valuable due to their hard validation of HBM biofidelity at the tissue response level. Consequently, achieving accurate chest injury assessment through HBM technology represents a critical biomechanical research challenge. This study aims to identify the optimal method for defining thorax deflection in HBM to improve injury assessment in vehicle-pedestrian collisions.

methodsA simulation matrix was designed, incorporating 5 different vehicle front-end types, 3 impact angles, 4 collision speeds, and 2 pedestrian postures, resulting in a total of 120 pedestrian-vehicle collision simulations. The simulations utilized the Total Human Model for Safety version 4.02 pedestrian model and predefined chest bands spanning from the upper to the lower chest. A total of 4 distinct methods for defining chest deflection were tested across the simulations. Principal component analysis was applied for dimensionality reduction to further refine the analysis. Two biomechanically distinct injury risk functions were employed to assess chest injury: the strain-based method, incorporating principal strain distributions across 12 ribs, and the deflection-based method, based on our pre-defined 6 chest band measurements.

resultsOur experimental findings reveal that injury risk decreases initially as the bonnet leading edge height increases, reaching a minimum around 950 mm with abbreviated injury scale 3+ and the number of fractured ribs (NFR) 3+ risk below 0.2, and then increasing thereafter. The study also demonstrated minimal chest injury risk acrossNFR2+/NFR3+ and NFR7+ conditions, with NFR risk below 0.2, despite localized high rib strain observed in certain individual ribs up to 8.28 × 10

conclusionThis study successfully determined the optimal method for measuring thorax deflection for HBM chest injury prediction, which could serve as a complementary metric for pedestrian chest injury assessment.

Indexed as

Accidents, TrafficModels, BiologicalPedestriansThoracic InjuriesThoraxBiomechanical PhenomenaHumansHuman body modellingInjury assessmentInjury criterionPassive safetyPedestrian thorax impactor

Identifiers

PMID41644391
PMCPMC13654433

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.