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

Fang Wang,Zhen Wang,Lin Hu,Chao Yu,Hongzhen Xu,Fan Li

doi:10.3389/fbioe.2021.677982

Abstract

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.

Highlights

Traumatic brain injury (TBI) has become a global health problem due to its corresponding high fatality and disability rates (Corrigan et al, 2010)
In view of the above deficiencies, we proposed a coupled finite element (FE)–MB human body model [coupled pedestrian computational biomechanics model (CPCBM)] in our previous study (Yu et al, 2020), where it was confirmed that the risk of brain injury in an accident is lower than the real injury when only the head model is used to reconstruct the accident (Wang et al, 2020)
The coupled FE–MB human body model was used to simulate vulnerable road user (VRU) injury in real traffic accidents, kinematics reconstruction, and head/brain injury reproduction of a series of real-world car-to-VRU impact accidents to investigate the effectiveness of various head injury criterion (HIC) in predicting the head injury risk due to VRU–car collision

Summary

Introduction

Traumatic brain injury (TBI) has become a global health problem due to its corresponding high fatality and disability rates (Corrigan et al, 2010). Statistics show that about 10 million people suffer from TBI each year worldwide (Fahlstedt et al, 2016). Deaths due to TBI were reported to account for 40% of all deaths annually, and TBI is the main reason for mortality under the age Effectiveness of Head Injury Criteria of 45 in the United States. The incidence of TBI in the population of young people (15–30 years) was 154–415/100,000 in the United States, 535/100,000 in France, and 240/100,000 in Australia (Popescu et al, 2015). There is no ongoing large-scale epidemiological investigation of TBIs in China; according to statistics based on the national TBI database, the mortality rate of patients hospitalized for TBI is known as 27.23% (Yang et al, 2017). Studies on TBIs in traffic accidents have great practical significance

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Discussion

Conclusion