Common functional principal components analysis: A new approach to analyzing human movement data
Common functional principal components analysis: A new approach to analyzing human movement data
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DOI：
10.1016/j.humov.2010.11.005
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发表时间：
2011-12-01
影响因子：
2.1
通讯作者：
Hayes, K.
中科院分区：
文献类型：
--
作者：
Coffey, N.;Harrison, A. J.;Hayes, K.
在许多人类运动研究中，测量了几组个体的角度-时间序列数据。目前比较各组的方法包括比较各组的平均值或使用多变量技术，如主成分分析和对主成分得分进行测试。这些方法虽然丢弃了大量的信息，但很有用。功能数据分析是人体运动研究中一种新兴的统计分析技术，它将角度-时间序列数据视为一个函数而不是一系列离散的测量值。这种方法保留了数据中的所有信息。功能主成分分析（FPCA）是多元主成分分析的扩展，它检查曲线样本的变异性，并已被用于检查几组个体的运动模式的差异。目前，每个组的功能主成分（FPC）要么单独确定（产生特定于组的成分），要么通过组合所有组的数据并确定组合数据的FPC（产生汇总整个数据集的成分）。组特异性FPC包含组内和组间变异，当各组中FPC的顺序发生变化时，在比较各组之间的FPC时会出现问题。组合数据的FPC可能无法充分描述所有个体组，组间比较通常使用每组平均FPC评分的t检验。当这些差异在统计学上不显著时，可能难以确定特定干预如何影响运动模式或受伤受试者与对照组有何不同。在本文中，我们的目标是执行FPCA的方式，允许明智的曲线组之间的比较。一种被称为公共功能主成分分析（CFPCA）的统计技术。CFPCA识别了组间差异的共同来源，但允许每个组件的顺序为特定组而改变。这允许跨组直接比较组件。我们使用我们的方法来分析生物力学数据集检查慢性跟腱损伤的机制和矫形器的功能效果。(C)2011 Elsevier B. V.保留所有权利。
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. (C) 2011 Elsevier B.V. All rights reserved.