Flat foot classification using principal component analysis applied to ground reaction forces

A. Bertani, A. Cappello, F. Catani, M. G. Benedetti, S. Giannini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Statistical pattern recognition techniques were applied to discriminate between healthy and flat foot children through ground reaction force (GRF) measurements, 28 young subjects (age 11.8±1.8) with symptomatic flexible flat foot, and 13 healthy subjects (age 10.1±1.4) took part on this preliminar study. The Karhunen-Loeve expansion with Fukunaga-Koontz normalization was applied to each GRF component to extract concise and discriminant features from the original data. The best performances of the classifying process were obtained using three principal components of both vertical and fore-aft GRF projections. Both linear and quadratic Bayes classifiers were designed with 9.2% and 4.9% total this misclassification errors for vertical, and 14.9% and 9.5% for fore-aft projections, respectively. Analysis of correlation between the patterns of each class and the first principal component demonstrates that the most representative factors in the functional diagnosis of flat foot are the less absorption in the fore-aft component and the reduction of the second vertical peak during push-off.

Original languageEnglish
Title of host publicationInternational Conference on Simulations in Biomedicine, Proceedings, BIOMED
EditorsH. Power, C.A. Brebbia, J. Kenny
PublisherComputational Mechanics Publ
Pages259-268
Number of pages10
Publication statusPublished - 1997
EventProceedings of the 1997 4th International Conference on Simulations in Biomedicine, BIOMED - Acquasparta, Italy
Duration: Jun 9 1997Jun 11 1997

Other

OtherProceedings of the 1997 4th International Conference on Simulations in Biomedicine, BIOMED
CityAcquasparta, Italy
Period6/9/976/11/97

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Flat foot classification using principal component analysis applied to ground reaction forces'. Together they form a unique fingerprint.

Cite this