PCA of waveforms and functional PCA: a primer for biomechanics

John Warmenhoven, Norma Bargary, Dominik Liebl, Andrew Harrison, Mark A. Robinson, Edward Gunning, Giles Hooker

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


Principal components analysis (PCA) of waveforms and functional PCA (fPCA) are statistical approaches used to explore patterns of variability in biomechanical curve data, with fPCA being an accepted statistical method grounded within the functional data analysis (FDA) statistical framework. This technical note demonstrates that PCA of waveforms is the most rudimentary form of FDA, and consequently can be rationalised within the FDA framework of statistical processes. Mathematical proofing applied demonstrations of both techniques, and an example of when fPCA may be of greater benefit to control over smoothing of functional principal components is provided using an open access motion sickness dataset. Finally, open access software is provided with this paper as means of priming the biomechanics community for using these methods as a part of future functional data explorations.

Original languageEnglish
Article number110106
Pages (from-to)1-6
Number of pages6
JournalJournal of Biomechanics
Publication statusPublished - 12 Feb 2021
Externally publishedYes


Dive into the research topics of 'PCA of waveforms and functional PCA: a primer for biomechanics'. Together they form a unique fingerprint.

Cite this