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Predicting Cumulative and Maximum Brain Strain Measures From HybridIII Head Kinematics: A Combined Laboratory Study and Post-Hoc Regression Analysis

Predicting Cumulative and Maximum Brain Strain Measures From HybridIII Head Kinematics: A... Due to growing concern on brain injury in sport, and the role that helmets could play in preventing brain injury caused by impact, biomechanics researchers and helmet certification organizations are discussing how helmet assessment methods might change to assess helmets based on impact parameters relevant to brain injury. To understand the relationship between kinematic measures and brain strain, we completed hundreds of impacts using a 50th percentile Hybrid III head-neck wearing an ice hockey helmet and input three-dimensional impact kinematics to a finite element brain model called the Simulated Injury Monitor (SIMon) (n = 267). Impacts to the helmet front, back and side included impact speeds from 1.2 to 5.8 ms−1. Linear regression models, compared through multiple regression techniques, calculating adjusted R 2 and the F-statistic, determined the most efficient set of kinematics capable of predicting SIMon-computed brain strain, including the cumulative strain damage measure (specifically CSDM-15) and maximum principal strain (MPS). Resultant change in angular velocity, Δω R, better predicted CSDM-15 and MPS than the current helmet certification metric, peak g, and was the most efficient model for predicting strain, regardless of impact location. In nearly all cases, the best two-variable model included peak resultant angular acceleration, α R, and Δω R. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Biomedical Engineering Springer Journals

Predicting Cumulative and Maximum Brain Strain Measures From HybridIII Head Kinematics: A Combined Laboratory Study and Post-Hoc Regression Analysis

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References (23)

Publisher
Springer Journals
Copyright
Copyright © 2017 by The Author(s)
Subject
Biomedicine; Biomedicine, general; Biomedical Engineering; Biological and Medical Physics, Biophysics; Classical Mechanics; Biochemistry, general
ISSN
0090-6964
eISSN
1573-9686
DOI
10.1007/s10439-017-1848-y
pmid
28497321
Publisher site
See Article on Publisher Site

Abstract

Due to growing concern on brain injury in sport, and the role that helmets could play in preventing brain injury caused by impact, biomechanics researchers and helmet certification organizations are discussing how helmet assessment methods might change to assess helmets based on impact parameters relevant to brain injury. To understand the relationship between kinematic measures and brain strain, we completed hundreds of impacts using a 50th percentile Hybrid III head-neck wearing an ice hockey helmet and input three-dimensional impact kinematics to a finite element brain model called the Simulated Injury Monitor (SIMon) (n = 267). Impacts to the helmet front, back and side included impact speeds from 1.2 to 5.8 ms−1. Linear regression models, compared through multiple regression techniques, calculating adjusted R 2 and the F-statistic, determined the most efficient set of kinematics capable of predicting SIMon-computed brain strain, including the cumulative strain damage measure (specifically CSDM-15) and maximum principal strain (MPS). Resultant change in angular velocity, Δω R, better predicted CSDM-15 and MPS than the current helmet certification metric, peak g, and was the most efficient model for predicting strain, regardless of impact location. In nearly all cases, the best two-variable model included peak resultant angular acceleration, α R, and Δω R.

Journal

Annals of Biomedical Engineering – Springer Journals

Published: May 11, 2017

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