Comparative analyses of anthropometry associated with overweight and obesity: PCA and ICA approaches

This study undertakes to explore the co-varying structure in anthropometric variables that might be affected by the recent surge of overweight and obesity. The increase of overweight and obesity makes the distribution of body dimensions asymmetric by the long tail in distribution (skewness, kurtosis). Principal component analysis (PCA) has been well applied to understand the co-varying body dimensions. However, because PCA decomposes covariance/correlation matrix, the effects of overweight and obesity may not be well captured. Independent component analysis (ICA) is a variant of PCA with the additional assumptions of components being non-Gaussian and independent, in which kurtosis is decomposed. PCA and ICA are applied on the anthropometric data from the North American portion of the Civilian American and European Surface Anthropometry Resource (CAESAR) project. ICA yields more interpretable results by visual inspection than corresponding PCA results. The first independent component (IC 1) is associated with hip/thigh circumferences and chest/waist circumferences and has the largest correlation coefficients with body mass index (BMI). Only the second IC shows the overall size factor that reveals gender difference while principal components 1, 2 and 3 show gender difference. The ICs 3 (torso length) and 4 (arm and leg lengths) are associated with individual differences in body dimensions. The ranges of 38 body dimensions are identified in order to satisfactorily meet the anthropometric variations for both males and females. The ICA gives promise of becoming a valuable tool in the field of ergonomics.