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

Journal: Computational and mathematical methods in medicine
Published Date: Jan 1, 2017

Abstract

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.

Authors

  • Ruchi D Chande
    Department of Biomedical Engineering, Virginia Commonwealth University, 401 West Main Street, P.O. Box 843067, Richmond, VA 23284-3067, USA.
  • Rosalyn Hobson Hargraves
    Department of Electrical Engineering, Virginia Commonwealth University, 601 West Main Street, P.O. Box 843072, Richmond, VA 23284-3072, USA.
  • Norma Ortiz-Robinson
    Department of Mathematics & Applied Mathematics, Virginia Commonwealth University, 1015 Floyd Avenue, P.O. Box 842014, Richmond, VA 23284-2014, USA.
  • Jennifer S Wayne
    Department of Biomedical Engineering, Virginia Commonwealth University, 401 West Main Street, P.O. Box 843067, Richmond, VA 23284-3067, USA.

Keywords

External Resources

Popular Topics
Recent Journals