| Summary: | In an era where man-machine interaction increasingly uses remote sensing, gesture recognition through use of the micro-Doppler (mD) effect is an emerging application which has attracted great interest. It is a sensible solution and here the authors show its potential for detecting aperiodic human movements. In this study, the authors classify ten hand gestures with a set of handcrafted features using simulated mD signatures generated from Kinect skeleton data. Data augmentation in the form of synthetic minority oversampling technique has been applied to create synthetic samples and classified with the support vector machine and K-nearest neighbour classifier with classification rate of 71.1 and 51% achieved. Finally, using weights generated by an action pair based one vs. one classification layer improves classification accuracy by 24.7 and 28.4%.
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