New Distinct Component Patterns for Plantar Pressure Variables by Using Principal Component Analysis

Abstract

Background: It is important to determine the plantar pressure distribution of schoolchildren by applying static and dynamic foot analyses using a pedobarography device. However, it is difficult to obtain clear interpretations from results that can be explained by a large number of plantar pressure variables. The aim of this study was to use principal component analysis (PCA) to predict the main components for reducing the size of big data sets, provide a practical overview, and minimize information loss on the subject of plantar pressure assessment in youths. Methods: In total, 112 schoolchildren were included in the study (mean ± SD: age, 10.58 ± 1.27 years; body mass index, 18.86 ± 4.33). During the research, a pedobarography device was used to obtain plantar pressure data. Each foot was divided into six anatomical regions and evaluated. Global and regional plantar pressure distributions, load and surface areas, pressure-time integrals, weight ratios, and geometric foot properties were calculated. Results: The PCA yielded ten principal components that together account for 81.88% of the variation in the data set and represent new and distinct patterns. Thus, 137 variables affecting the subject were reduced to ten components. Conclusions: The numerous variables that affect static and dynamic plantar pressure distributions can be reduced to ten components by PCA, making the research results more concise and understandable.