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A Proposed Algorithm to Assess Concussion Potential in Rear-End Motor Vehicle Collisions: A Meta-Analysis
Joint Authors
Limousis-Gayda, Manon
Hashish, Rami
Source
Applied Bionics and Biomechanics
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-14
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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: head acceleration=0.465delta‐V1.3231 (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.
American Psychological Association (APA)
Limousis-Gayda, Manon& Hashish, Rami. 2020. A Proposed Algorithm to Assess Concussion Potential in Rear-End Motor Vehicle Collisions: A Meta-Analysis. Applied Bionics and Biomechanics،Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1120249
Modern Language Association (MLA)
Limousis-Gayda, Manon& Hashish, Rami. A Proposed Algorithm to Assess Concussion Potential in Rear-End Motor Vehicle Collisions: A Meta-Analysis. Applied Bionics and Biomechanics No. 2020 (2020), pp.1-6.
https://search.emarefa.net/detail/BIM-1120249
American Medical Association (AMA)
Limousis-Gayda, Manon& Hashish, Rami. A Proposed Algorithm to Assess Concussion Potential in Rear-End Motor Vehicle Collisions: A Meta-Analysis. Applied Bionics and Biomechanics. 2020. Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1120249
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1120249