Parallel factor analysis of gait waveform data: a multimode extension of principal component analysis
Helwig, Nathaniel
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https://hdl.handle.net/2142/29560
Description
Title
Parallel factor analysis of gait waveform data: a multimode extension of principal component analysis
Author(s)
Helwig, Nathaniel
Issue Date
2012-02-01T00:55:26Z
Director of Research (if dissertation) or Advisor (if thesis)
Hong, Sungjin
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.A.
Degree Level
Thesis
Keyword(s)
Walking
Locomotion
Multivariate analysis
Principal Component Analysis
Parallel Factor Analysis
Abstract
Gait data are typically collected in multivariate form, so some multivariate analysis is often used to understand interrelationships between observed data. Principal Component Analysis (PCA), a data reduction technique for correlated multivariate data, has been widely applied by gait analysts to investigate patterns of association in gait waveform data (e.g., interrelationships between joint angle waveforms from different subjects and/or joints). Despite its widespread use in gait analysis, PCA is for two-mode data, whereas gait data are often collected in higher-mode form. In this paper, we present the benefits of analyzing gait data via Parallel Factor Analysis (Parafac), which is a component analysis model designed for three- or higher-mode data. Using three-mode joint angle waveform data (subjects × time × joints), we demonstrate Parafac’s ability to (a) determine interpretable components revealing the primary interrelationships between lower-limb joints in healthy gait and (b) identify interpretable components revealing the fundamental differences between normal and perturbed subjects’ gait patterns across multiple joints. Our results offer evidence of the complex interconnections that exist between lower-limb joints and limb segments in both normal and abnormal gaits, confirming the need for the simultaneous analysis of multi-joint gait waveform data (especially when studying perturbed gait patterns).
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