Pattern classification of kinematic and kinetic running data to distinguish gender,shod/barefoot and injury groups with feature ranking |
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| Authors: | Bjoern M Eskofier Martin Kraus Jay T Worobets Darren J Stefanyshyn Benno M Nigg |
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| Institution: | 1. Human Performance Laboratory , Faculty of Kinesiology, University of Calgary , 2500 University Dr NW, Calgary , AB , Canada , T2N 1N4 bmeskofi@ucalgary.ca;3. Pattern Recognition Laboratory, Department of Computer Science , University of Erlangen , Martensstrasse 3, 91058 , Erlangen , Germany;4. Human Performance Laboratory , Faculty of Kinesiology, University of Calgary , 2500 University Dr NW, Calgary , AB , Canada , T2N 1N4 |
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| 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. |
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| Keywords: | pattern classification biomechanical group classification generic features feature ranking AdaBoost patellofemoral pain syndrome |
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