title: Common functional principal components analysis: a new approach to analyzing human movement data.
abstract: In many human movement studies angle-time series data on several groups of individuals are measured. Current methods to compare groups include comparisons of the mean value in each group or use multivariate techniques such as principal components analysis and perform tests on the principal component scores. Such methods have been useful, though discard a large amount of information. Functional data analysis (FDA) is an emerging statistical analysis technique in human movement research which treats the angle-time series data as a function rather than a series of discrete measurements. This approach retains all of the information in the data. Functional principal components analysis (FPCA) is an extension of multivariate principal components analysis which examines the variability of a sample of curves and has been used to examine differences in movement patterns of several groups of individuals. Currently the functional principal components (FPCs) for each group are either determined separately (yielding components that are group-specific), or by combining the data for all groups and determining the FPCs of the combined data (yielding components that summarize the entire data set). The group-specific FPCs contain both within and between group variation and issues arise when comparing FPCs across groups when the order of the FPCs alter in each group. The FPCs of the combined data may not adequately describe all groups of individuals and comparisons between groups typically use t-tests of the mean FPC scores in each group. When these differences are statistically non-significant it can be difficult to determine how a particular intervention is affecting movement patterns or how injured subjects differ from controls. In this paper we aim to perform FPCA in a manner allowing sensible comparisons between groups of curves. A statistical technique called common functional principal components analysis (CFPCA) is implemented. CFPCA identifies the common sources of variation evident across groups but allows the order of each component to change for a particular group. This allows for the direct comparison of components across groups. We use our method to analyze a biomechanical data set examining the mechanisms of chronic Achilles tendon injury and the functional effects of orthoses.
authors.affiliation: School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, Galway, Ireland. norma.coffey@nuigalway.ie
authors.forename: N
authors.initials: N
authors.lastname: Coffey
authors.forename: A J
authors.initials: AJ
authors.lastname: Harrison
authors.forename: O A
authors.initials: OA
authors.lastname: Donoghue
authors.forename: K
authors.initials: K
authors.lastname: Hayes
mesh_terms: D001696:Biomechanical Phenomena / Q000502:physiology*; D005684:Gait; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D007869:Leg Injuries / Q000503:physiopathology; D008962:Models, Theoretical; D009989:Orthotic Devices; D025341:Principal Component Analysis; D012016:Reference Values; D015203:Reproducibility of Results; D012420:Running / Q000502:physiology; D016474:Weight-Bearing / Q000502:physiology