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.
The original version of this chapter was inadvertently published with an incorrect chapter pagination 574–579 and DOI 10.1007/978-3-319-32703-7_112. The page range and the DOI has been re-assigned. The correct page range is 580–585 and the DOI is 10.1007/978-3-319-32703-7_113. The erratum to this chapter is available at DOI: 10.1007/978-3-319-32703-7_260
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-32703-7_260
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Ferber R, Davis I M, Williams III D S (2003) Gender differences in lower extremity mechanics during running. Clin Biomech 18:350–357
Phinyomark A, Hettinga B A, Osis S T et al (2014) Gender and age-related differences in bilateral lower extremity mechanics during treadmill running. PLoS One 9:e105246
Eskofier B M, Kraus M, Worobets J T et al (2012) Pattern classification of kinematic and kinetic running data to distinguish gender, shod/barefoot and injury groups with feature ranking. Comput Methods Biomech Biomed Eng 15:467–474
Brandon S C E, Graham R B, Almosnino S et al (2013) Interpreting principal components in biomechanics: Representative extremes and single component reconstruction. J Electromyogr Kinesiol 23:1304–1310
Phinyomark A, Hettinga B A, Osis S et al (2015) Do intermediate- and higher-order principal components contain useful information to detect subtle changes in lower extremity biomechanics during running? Hum Mov Sci 44:91–101
Foch E, Milner C E (2014) The influence of iliotibial band syndrome history on running biomechanics examined via principal component analysis. J Biomech 47:81–86
Phinyomark A, Osis S, Hettinga B A et al (2015) Gender differences in gait kinematics in runners with iliotibial band syndrome. Scand J Med Sci Sports 25:744–753
Louw M, Deary C (2014) The biomechanical variables involved in the aetiology of iliotibial band syndrome in distance runners - a systematic review of the literature. Phys Ther Sport 15:64–75
Jones MC, Rice JA (1992) Displaying the important features of large collections of similar curves. Am Stat 46:140–145
Taylor R (1990) Interpretation of the correlation coefficient: a basic review. JDMS 1:35–39
Cohen J (1988) Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associate, Hillsdale, NJ
Fukuchi R K, Eskofier B M, Duarte M et al (2011) Support vector machines for detecting age-related changes in running kinematics. J Biomech 44:540–542
Deluzio KJ, Wyss UP, Zee B et al (1997) Principal component models of knee kinematics and kinetics: normal vs. pathological gait patterns. Hum Mov Sci 16:201–217
Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene expression data. J Bioinf Comput Biol 3:185–205
Phinyomark A, Phukpattaranont P, Limsakul C (2012) Feature reduction and selection for EMG signal classification. Expert Syst Appl 39:7420–7431
Phinyomark A, Phukpattaranont P, Limsakul C (2012) Fractal analysis features for weak and single-channel upper-limb EMG signal. Expert Syst Appl 39:11156–11163.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Phinyomark, A., Osis, S.T., Kobsar, D., Hettinga, B.A., Leigh, R., Ferber, R. (2016). Biomechanical Features of Running Gait Data Associated with Iliotibial Band Syndrome: Discrete Variables Versus Principal Component Analysis. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_113
Download citation
DOI: https://doi.org/10.1007/978-3-319-32703-7_113
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32701-3
Online ISBN: 978-3-319-32703-7
eBook Packages: EngineeringEngineering (R0)