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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 3602928, 8 pages
Research Article

Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks

1Department of Biomedical Engineering, Virginia Commonwealth University, 401 West Main Street, P.O. Box 843067, Richmond, VA 23284-3067, USA
2Department of Electrical Engineering, Virginia Commonwealth University, 601 West Main Street, P.O. Box 843072, Richmond, VA 23284-3072, USA
3Department of Mathematics & Applied Mathematics, Virginia Commonwealth University, 1015 Floyd Avenue, P.O. Box 842014, Richmond, VA 23284-2014, USA

Correspondence should be addressed to Jennifer S. Wayne; ude.ucv@enyawj

Received 15 September 2016; Revised 15 December 2016; Accepted 20 December 2016; Published 30 January 2017

Academic Editor: Kaan Yetilmezsoy

Copyright © 2017 Ruchi D. Chande et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model.