13 May 2016 Gender classification of running subjects using full-body kinematics
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This paper proposes novel automated gender classification of subjects while engaged in running activity. The machine learning techniques include preprocessing steps using principal component analysis followed by classification with linear discriminant analysis, and nonlinear support vector machines, and decision-stump with AdaBoost. The dataset consists of 49 subjects (25 males, 24 females, 2 trials each) all equipped with approximately 80 retroreflective markers. The trials are reflective of the subject’s entire body moving unrestrained through a capture volume at a self-selected running speed, thus producing highly realistic data. The classification accuracy using leave-one-out cross validation for the 49 subjects is improved from 66.33% using linear discriminant analysis to 86.74% using the nonlinear support vector machine. Results are further improved to 87.76% by means of implementing a nonlinear decision stump with AdaBoost classifier. The experimental findings suggest that the linear classification approaches are inadequate in classifying gender for a large dataset with subjects running in a moderately uninhibited environment.
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Christina M. Williams, Christina M. Williams, Jeffrey B. Flora, Jeffrey B. Flora, Khan M. Iftekharuddin, Khan M. Iftekharuddin, "Gender classification of running subjects using full-body kinematics", Proc. SPIE 9841, Geospatial Informatics, Fusion, and Motion Video Analytics VI, 984107 (13 May 2016); doi: 10.1117/12.2225084; https://doi.org/10.1117/12.2225084

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