Bayesian analysis of human movement curves

• Published in: Applied statistics Vol. 56; no. 4; pp. 415 - 428
• Main Authors: Alshabani, A. K. S., Dryden, I. L., Litton, C. D., Richardson, J.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01-08-2007; Blackwell Publishers; Blackwell; Royal Statistical Society; Oxford University Press
• Series: Journal of the Royal Statistical Society Series C
• Subjects: Algorithms; Applications; Bayesian analysis; Bayesian networks; Correlation; Curvature; Data analysis; Data smoothing; Distribution theory; Exact sciences and technology; Fingers; Functional data; Gibbs sampling; Hastings algorithm; Human behaviour; Human movement; Landmarks; Markov chain Monte Carlo methods; Markovian processes; Mathematics; Metropolis; Missing data; Monte Carlo simulation; Movement; Multivariate analysis; Orthographic projections; Probability and statistics; Probability theory and stochastic processes; Procrustes analysis; Sciences and techniques of general use; Shape; Social science research; Statistical analysis; Statistical methods; Statistics; Studies; Trajectories; Variance analysis; Bayes estimation; Human; Data analysis; Correlation; Three-dimensional calculations; Shape; Image processing; Functional data; Metropolis-Hastings algorithm; Covariate; Gibbs sampling; Algorithm; Mean estimation; Statistics; Missing data; Distribution function; Hierarchical model; Markov chain Monte Carlo methods; Application; Procrustes analysis; Posterior distribution; Human movement
• DOI: 10.1111/j.1467-9876.2007.00584.x
• EISSN: 1467-9876
• ISSN: 0035-9254

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.