Functional data analysis of knee joint kinematics in the vertical jump.
Understanding of the motor development process is usually based on descriptive studies using either cross-sectional or longitudinal designs. These data typically consist of sets of measurements on groups of individuals gathered during the performance of a single task. A natural approach is to represent the set of measurements for an individual as a single entity, a function. In practice, however, this approach is seldom applied. Typically, the analysis proceeds by reducing what are intrinsically functional responses to a single summary measurement and then using this to draw conclusions. As a result, many potentially informative data are ignored. Functional data analysis (FDA) is an emerging field in statistics that focuses on treating an entire sequence of measurements for an experimental unit as a single function. Therefore, functional data analysis appears to be inherently suitable for analysing kinematic data. In this paper, the key concepts and procedures of functional data analysis are introduced and illustrated using data obtained from a cross-sectional study on the development of the vertical jump.
Ryan
Willie
W
Department of Mathematics and Statistics, University of Limerick, Ireland.
Harrison
Andrew
A
Hayes
Kevin
K
D001696:Biomechanical Phenomena; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007719:Knee Joint / Q000502:physiology*; D008124:Locomotion / Q000502:physiology*; D008297:Male; D025341:Principal Component Analysis