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Deep Learning/Neural Network

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

namepen 2017. 10. 11. 22:52

https://www.ncbi.nlm.nih.gov/pubmed/28250804



 2017;2017:3602928. doi: 10.1155/2017/3602928. Epub 2017 Jan 30.

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

Chande RD1, Hargraves RH2, Ortiz-Robinson N3, Wayne JS1.

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.

PMID:
 
28250804
 
PMCID:
 
PMC5304311
 
DOI:
 
10.1155/2017/3602928






CMMM2017-3602928.pdf


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