Bayesian analysis of human movement curves

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 descr...

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Bibliographic Details
Published in:Applied statistics 2007-08, Vol.56 (4), p.415-428
Main Authors: Alshabani, A. K. S., Dryden, I. L., Litton, C. D., Richardson, J.
Format: Article
Language:English
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
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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.
ISSN:0035-9254
1467-9876
DOI:10.1111/j.1467-9876.2007.00584.x