Pattern classification of kinematic and kinetic running data to distinguish gender, shod/barefoot and injury groups with feature ranking

Bjoern Eskofier, Martin Kraus, Jay Worobets, Darren Stefanyshyn and Benno Nigg

Computer Methods in Biomechanics and Biomedical Engineering, 2012, vol. 15, issue 5, 467-474

Abstract: The identification of differences between groups is often important in biomechanics. This paper presents group classification tasks using kinetic and kinematic data from a prospective running injury study. Groups composed of gender, of shod/barefoot running and of runners who developed patellofemoral pain syndrome (PFPS) during the study, and asymptotic runners were classified.The features computed from the biomechanical data were deliberately chosen to be generic. Therefore, they were suited for different biomechanical measurements and classification tasks without adaptation to the input signals. Feature ranking was applied to reveal the relevance of each feature to the classification task.Data from 80 runners were analysed for gender and shod/barefoot classification, while 12 runners were investigated in the injury classification task. Gender groups could be differentiated with 84.7%, shod/barefoot running with 98.3%, and PFPS with 100% classification rate. For the latter group, one single variable could be identified that alone allowed discrimination.

Date: 2012
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DOI: 10.1080/10255842.2010.542153

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