Toward Development of a Single-Degree-of-Freedom Mechanical Model for Predicting Brain Injury

In this study, the authors go back to the fundamentals of injury biomechanics and develop a single‐degree‐of‐freedom (sDOF) mechanical system to explore the relationship between rotational head kinematics and brain tissue deformation for the development of brain injury criteria. Efficacy of the sDOF model for predicting brain injury response was assessed by comparing the dynamic response of the model to strain‐based brain injury metrics obtained from a finite element (FE) model of the head. The authors believe this is the correct approach for developing robust brain injury criteria based on rotational head kinematics and applicable in a broad range of impacts. Based on this preliminary assessment using the FE model for pure coronal plane rotations, the results indicate that a sDOF mechanical system may sufficiently predict strain‐based brain injury responses measured in the more complicated FE model. In addition, these results suggest that knowledge of both head angular velocity and acceleration may be required to accurately predict brain injury responses over a large range of impact durations. Further, the response surfaces observed in the current study are also similar to the DAI tolerance curves proposed in the literature for primate and human, which suggests that rotational brain mechanics behaves in a manner that may be adequately explained by fundamental mechanical models.

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  • Supplemental Notes:
    • Abstract reprinted with permission of the International Council on the Biomechanics of Injury (IRCOBI).
  • Corporate Authors:

    International Research Council on Biomechanics of Injury (IRCOBI)

    Winkelriedstrasse 27
    Zurich,   Switzerland  CH-8006
  • Authors:
    • Gabler, Lee F
    • Joodaki, Hamed
    • Crandall, Jeff R
    • Panzer, Matthew B
  • Conference:
  • Publication Date: 2016

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  • English

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  • Accession Number: 01611943
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 18 2016 2:54PM