Emerson, N.J., Carré, M.J. orcid.org/0000-0003-3622-990X, Reilly, G.C. orcid.org/0000-0003-1456-1071 et al. (1 more author) (2013) Simulation based upon medical data offers a fast and robust method for the prediction of fracture risk. Procedia Engineering, 60. pp. 459-466. ISSN 1877-7058
Abstract
The accurate estimation of activation forces remains a significant challenge in the field of injury prediction and simulation in sports. Precision in the field of biomechanical simulation has been improved through the use of medical data such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). These have the added benefit of providing simulation that is patient-specific. As a developing research technique, the absolute accuracy of biomechanical simulation has been improved in line with the development of both imaging and simulation technology. Cutting-edge simulation methods are now able to describe the minutiae of biomechanical systems with ever-increasing complexity. As the complexity of progressive biomechanical simulation increases, research is being undertaken to determine if more simplistic methods may now be considered for the robust and accurate portrayal of general bone behaviour and fracture prediction. In this paper, the Computed Tomography based Finite Element (CT- FE) simulation process is examined and its application with regards to Sports Engineering is discussed. It is proposed that this method of patient-specific and geometrically-accurate simulation would provide an excellent tool for the investigation of injury mechanisms and equipment design, allowing a wide array of operating conditions to be simulated without the need for physical testing, which can be complex to the extent of unfeasible.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2013 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license. |
Keywords: | Finite element; mechanical model torsion; compression; simulation; bone; porcine; femur |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Materials Science and Engineering (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Human Metabolism (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Apr 2016 11:03 |
Last Modified: | 14 Apr 2016 11:03 |
Published Version: | http://dx.doi.org/10.1016/j.proeng.2013.07.051 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.proeng.2013.07.051 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:98044 |