Tierney, GJ orcid.org/0000-0002-4666-4473 and Simms, C (2019) Predictive capacity of the MADYMO Multibody human body model applied to head kinematics during rugby union tackles. Applied Sciences, 9 (4). 726. ISSN 2076-3417
Abstract
Multibody models have not yet been evaluated for reconstructing head kinematics during sports impacts. Accordingly, the goal of this study was to utilise whole-body motion data from twenty upper and mid/lower trunk rugby shoulder tackles recorded in a marker-based 3D motion analysis laboratory to assess the MADYMO human body passive ellipsoid model for head kinematic reconstruction. Head linear and angular velocity during the tackle for the multibody model predictions and 3D motion laboratory measures were recorded for the ball carrier. Examined were the linear and angular velocity, as well as the absolute and percentage differences. For upper trunk tackles, the median percentage error (with quartiles) for the MADYMO predictions were 10% (6% to 45%) and 23% (16% to 39%) for change in head linear and angular velocity, respectively. For mid/lower trunk tackles, the median percentage error (with quartiles) for the MADYMO predictions were 46% (33% to 63%) and 60% (53% to 123%) for change in head linear and angular velocity, respectively. In conclusion, the model is currently unsuitable for reconstruction of head kinematics during individual rugby union tackle cases.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | concussion; sport; computational modelling |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Biomedical Sciences (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Mar 2019 11:27 |
Last Modified: | 15 Mar 2019 11:27 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/app9040726 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:143460 |