When studying human kinematics, the intention is usually to isolate and model the movements of bony segments. With motion capture based on skin mounted marker systems, there is confounding of the extent to which the surface represents the movements of the underlying bony structures1 . Soft tissue artifact (STA) is an important confounder, caused by the relative movement between the skin mounted markers and the underlying bones. STA is a commonly encountered problem in biomechanics2 . Furthermore, for applications such as motion analysis of composite joints in the spine, wrist and ankle, the inability to associate surface markers closely with underlying bony segments has inhibited the construction of meaningful mechanical models3. The research question underpinning this program of work is whether it may be possible to use data from cross sectional imaging modalities in combination with surface mounted marker sets, to solve for the error associated with surface markers and so produce refined functional models of activities in real world settings.

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