Article

Changes in gait kinematics have often been analyzed using pattern recognition
methods such as principal component analysis (PCA). It is usually just the first
few principal components that are analyzed, because they describe the main
variability within a dataset and thus represent the main movement patterns.
However, while subtle changes in gait pattern (for instance, due to different
footwear) may not change main movement patterns, they may affect movements
represented by higher principal components. This study was designed to test two
hypotheses: (1) speed and gender differences can be observed in the first
principal components, and (2) small interventions such as changing footwear
change the gait characteristics of higher principal components. Kinematic changes
due to different running conditions (speed - 3.1m/s and 4.9 m/s, gender, and
footwear - control shoe and adidas MicroBounce shoe) were investigated by
applying PCA and support vector machine (SVM) to a full-body reflective marker
setup. Differences in speed changed the basic movement pattern, as was reflected
by a change in the time-dependent coefficient derived from the first principal.
Gender was differentiated by using the time-dependent coefficient derived from
intermediate principal components. (Intermediate principal components are
characterized by limb rotations of the thigh and shank.) Different shoe
conditions were identified in higher principal components.
This study showed that
different interventions can be analyzed using a full-body kinematic approach.
Within the well-defined vector space spanned by the data of all subjects, higher
principal components should also be considered because these components show the
differences that result from small interventions such as footwear changes.
CI - Crown Copyright (c) 2012. Published by Elsevier B.V. All rights reserved.

Langue : ANGLAIS