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Bayesian analysis of human movement curves

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  • A. K. S. Alshabani
  • I. L. Dryden
  • C. D. Litton
  • J. Richardson

Abstract

Summary. We consider the Bayesian analysis of human movement data, where the subjects perform various reaching tasks. A set of markers is placed on each subject and a system of cameras records the three‐dimensional Cartesian co‐ordinates of the markers during the reaching movement. It is of interest to describe the mean and variability of the curves that are traced by the markers during one reaching movement, and to identify any differences due to covariates. We propose a methodology based on a hierarchical Bayesian model for the curves. An important part of the method is to obtain identifiable features of the movement so that different curves can be compared after temporal warping. We consider four landmarks and a set of equally spaced pseudolandmarks are located in between. We demonstrate that the algorithm works well in locating the landmarks, and shape analysis techniques are used to describe the posterior distribution of the mean curve. A feature of this type of data is that some parts of the movement data may be missing—the Bayesian methodology is easily adapted to cope with this situation.

Suggested Citation

  • A. K. S. Alshabani & I. L. Dryden & C. D. Litton & J. Richardson, 2007. "Bayesian analysis of human movement curves," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(4), pages 415-428, August.
    • Handle: RePEc:bla:jorssc:v:56:y:2007:i:4:p:415-428
    DOI: 10.1111/j.1467-9876.2007.00584.x
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    References listed on IDEAS

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    1. Daniel Gervini & Theo Gasser, 2004. "Self‐modelling warping functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 959-971, November.
    2. Daniel Gervini & Theo Gasser, 2005. "Nonparametric maximum likelihood estimation of the structural mean of a sample of curves," Biometrika, Biometrika Trust, vol. 92(4), pages 801-820, December.
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