A Proposed Algorithm to Assess Concussion Potential in Rear-End Motor Vehicle Collisions: A Meta-Analysis

Author:

Limousis-Gayda Manon1ORCID,Hashish Rami1

Affiliation:

1. National Biomechanics Institute, Los Angeles, CA 90403, USA

Abstract

Concussions represent an increasing economic burden to society. Motor vehicle collisions (MVCs) are of the leading causes for sustaining a concussion, potentially due to high head accelerations. The change in velocity (i.e., delta-V) of a vehicle in a MVC is an established metric for impact severity. Accordingly, the purpose of this paper is to analyze findings from previous research to determine the relation between delta-V and linear head acceleration, including occupant parameters. Data was collected from previous research papers comprising both linear head acceleration and delta-V at the time of incident, head position of the occupant, awareness of the occupant prior to impact, as well as gender, age, height, and weight. Statistical analysis revealed the following significant power relation between delta-V and head acceleration: headacceleration=0.465deltaV1.3231 (R2=0.5913, p<0.001). Further analysis revealed that alongside delta-V, the occupant’s gender and head position prior to impact were significant predictors of head acceleration (p=0.022 and p=0.001, respectively). The strongest model developed in this paper is considered physiologically implausible as the delta-V corresponding to a theoretical concussion threshold of 80 g exceeds the delta-V associated with probability of fatality. Future research should be aimed at providing a more thorough data set of the occupant head kinematics in MVCs to help develop a stronger predictive model for the relation between delta-V and head linear and angular acceleration.

Publisher

Hindawi Limited

Subject

Biomedical Engineering,Bioengineering,Medicine (miscellaneous),Biotechnology

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Kendo Headgear Concussion Safety Evaluation;2023 8th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS);2023-11-23

2. A stochastic model validated with human test data causally associating target vehicle Delta V, occupant cervicocranial biomechanics, and injury during rear-impact crashes;Journal of Forensic and Legal Medicine;2022-10

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