Author/Authors :
Chande, Ruchi D Department of Biomedical Engineering - Virginia Commonwealth University - Richmond, USA , Hargraves, Rosalyn Hobson Department of Electrical Engineering - Virginia Commonwealth University - Richmond, USA , Ortiz-Robinson, Norma Department of Mathematics & Applied Mathematics - Virginia Commonwealth University - Richmond, USA , Wayne, Jennifer S Department of Biomedical Engineering - Virginia Commonwealth University - Richmond, USA
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.
