Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data are not always available for all the soft tissues nor is it known for patient-specific work. In the current research, a method to improve the ligament stiffness definition for a computational foot/ankle model was sought with the greater goal of improving the predictive ability of the computational model. Specifically, the stiffness values were optimized using artificial neural networks (ANNs); both feedforward and radial basis function networks (RBFNs) were considered. Optimal networks of each type were determined and subsequently used to predict stiffnesses for the foot/ankle model. Ultimately, the predicted stiffnesses were considered reasonable and resulted in enhanced performance of the computational model, suggesting that artificial neural networks can be used to optimize stiffness inputs.
Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model
Virginia Commonwealth University,
401 West Main Street,
PO Box 843067,
Richmond, VA 23284-3067
Manuscript received December 16, 2016; final manuscript received June 8, 2017; published online July 7, 2017. Assoc. Editor: Guy M. Genin.
- Views Icon Views
- Share Icon Share
- Cite Icon Cite
- Search Site
Chande, R. D., and Wayne, J. S. (July 7, 2017). "Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model." ASME. J Biomech Eng. September 2017; 139(9): 091003. doi: https://doi.org/10.1115/1.4037101
Download citation file:
- Ris (Zotero)
- Reference Manager