Biomechanical Features of Running Gait Data Associated with Iliotibial Band Syndrome: Discrete Variables Versus Principal Component Analysis

Abstract

The features associated with temporal gait biomechanical data are complex and multivariate and it is therefore necessary to identify methods that reduce the difficulty underlying the interpretation and identification of differences between groups of interest. Discrete variables and principal component analysis (PCA) are feature extraction methods that have been widely used. However, a comprehensive understanding of the relationship between discrete variables and PCA features has never been completed. The objectives of this study were to (1) determine the relationships between the two feature methods and (2) compare the performance of each for the identification and discrimination of between-group differences for injured and non-injured subjects. Running gait kinematic data of 48 patients experiencing iliotibial band syndrome (ITBS) were compared to a group of 48 asymptomatic control subjects for transverse plane hip and ankle joint and frontal plane hip joint waveform data. Twenty-two discrete variables and three to four PCA features were extracted from each waveform and divided into three subgroups: magnitude features, difference operator features, and phase shift features. The following key results were obtained: (1) strong correlations were found between discrete variables; (2) the first PCA feature captured the magnitude information and thus showed strong correlation with the discrete variables in the magnitude group; (3) there was no consistent result that showed all discrete variables were found in the first few principal components; (4) the performance of the PCA features in identifying between-group differences decreased (reduced the effect size) as compared to using the discrete variables, but this does not necessarily result in a decrease in the performance of the PCA features to discriminate between ITBS and controls using a support vector machine classifier. These results suggest care must be taken when selecting features of gait waveforms for both identification and discrimination of between-group differences for injured and non-injured runners.