A Unit Quaternion Based SOM for Anatomical Joint Constraint Modelling

Jenkins, Glenn L.; Dacey, Michael

doi:10.1109/uksim.2014.21

Cited by 3 publications

(2 citation statements)

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“…To overcome this issue, ANNs trained using unsupervised techniques such as competitive learning have been proposed. SOMs have been trained using competitive learning to implicitly model joint constraints using only valid orientations expressed as unit quaternions [19,20]. The weights of the output nodes are trained via competative learning to represent the training data while preserving the topography of the input space.…”

Section: Introductionmentioning

confidence: 99%

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Anatomical Joint Constraint Modelling with Rigid Map Neural Networks

Jenkins¹,

Roger²,

Dacey³

et al. 2016

SNE

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The development of anatomical models both for individuals and groups are important for applications in animation, medicine and ergonomics. Recent approaches have utilised unit quaternions to represent orientations between limbs which eliminate singularities encountered in other rotational representations. As a result a number of unit quaternion based joint constraint validation and correction methods have been developed. Recent approaches harness machine learning techniques to model valid orientation spaces and has included the use of Kohonen's Self Organizing Maps (SOMs) to model regular conical constraints on the orientation of the limb. Recent work has considered a derivative of the SOM, the Rigid Map, applied in the same context which we extend here.

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Section: Introductionmentioning

confidence: 99%

“…The network responds to a given input orientation with the closest orientation in its model of the input data. This can be used directly for correction [19] or as a target for an iterative approach [20].…”

Section: Introductionmentioning

confidence: 99%