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Analyzing individual and group differences in multijoint multiwaveform gait data using the Parafac2 model.

Nathaniel E Helwig¹, Sungjin Hong, Ehsan Bokhari

¹Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL 61820-6232, USA. nhelwig2@illinois.edu

International Journal for Numerical Methods in Biomedical Engineering

|January 8, 2013

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Principal Component Analysis is limited for analyzing complex locomotion data. Parafac2 effectively analyzes four-mode data, revealing distinct patterns in lower limb movement for both healthy and atypical gaits.

Area of Science:

Biomechanics
Multivariate Data Analysis
Human Locomotion

Background:

Locomotion research generates complex multiwaveform data from multiple body locations and subjects.
Traditional methods like Principal Component Analysis are limited as they are designed for two-mode data, not the higher-mode data typical in locomotion studies.

Purpose of the Study:

To present the advantages of the Parafac2 model for analyzing four-mode locomotion data (subjects × time × joints × waveforms).
To demonstrate Parafac2's capability in identifying fundamental kinematic patterns and inter-subject variations in lower limb movement.

Main Methods:

Application of the Parafac2 model to analyze four-mode locomotion data.
Utilized bilateral hip, knee, and ankle angular displacement, velocity, and acceleration waveforms.

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Compared fundamental patterns in healthy walking with differences in atypical gaits.

Main Results:

Parafac2 successfully generated interpretable components describing lower limb kinematics during walking.
Identified fundamental differences in multijoint, multiwaveform locomotive patterns between normal and atypical subjects.
Determined which specific waveforms characterize individual and group differences captured by each component.

Conclusions:

Parafac2 provides a powerful tool for the holistic analysis of complex, multi-mode locomotion data.
Different waveforms serve distinct analytical purposes, highlighting the need for comprehensive analysis, especially for atypical motion.
The findings underscore the importance of advanced multivariate techniques for understanding human movement.