Evolved Topology Generalized Multi-layer Perceptron (GMLP) for Anatomical Joint Constraint Modelling

Glenn, Jenkins and Dacey, Michael (2012) Evolved Topology Generalized Multi-layer Perceptron (GMLP) for Anatomical Joint Constraint Modelling. In: 14th International Conference on Computer Modelling and Simulation,, 28-30 March 2012, Cambridge.

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Abstract

The accurate simulation of anatomical joint models is becoming increasingly important for both medical diagnosis and realistic animation applications. Quaternion algebra has been increasingly applied to model rotations providing a compact representation while avoiding singularities. We propose the use of Artificial Neural Networks to accurately simulate joint constraints based on recorded data. This paper describes the application of Genetic Algorithm approaches to neural network training in order to model corrective piece-wise linear / discontinuous functions required to maintain valid joint configurations. The results show that artificial Neural Networks are capable of modeling constraints on the rotation of and around a virtual limb.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Citation: Jenkins, G., Dacey. M. E. "Evolved Topology Generalized Multi-layer Perceptron (GMLP) for Anatomical Joint Constraint Modelling", 14th International Conference on Computer Modelling and Simulation, Cambridge, pp. 107-112, 2012.
Uncontrolled Keywords: GMLP, Unit Quaternion, Constraint, Neural Network, Genetic Algorithm, Computing, Anatomy
Subjects: T Technology > T Technology (General)
Divisions: Faculties > Faculty of Architecture, Computing and Engineering > School of Applied Computing
Depositing User: John Dalling
Date Deposited: 17 Apr 2014 11:26
Last Modified: 30 Mar 2016 11:45
URI: http://repository.uwtsd.ac.uk/id/eprint/328

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