From Kinect skeleton data to hand gesture recognition with radar

Li, J., Shrestha, A., Le Kernec, J. and Fioranelli, F. (2019) From Kinect skeleton data to hand gesture recognition with radar. Journal of Engineering, 2019(20), pp. 6914-6919. (doi: 10.1049/joe.2019.0557)

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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%.

Item Type:Articles
Additional Information:A. Shrestha is supported for his PhD by the UK Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Award to the School of Engineering. The authors acknowledge support from the UK EPSRC through grant EP/R041679/1 INSHEP.
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco and Le Kernec, Dr Julien and Shrestha, Mr Aman
Authors: Li, J., Shrestha, A., Le Kernec, J., and Fioranelli, F.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Journal of Engineering
Publisher:Institution of Engineering and Technology
ISSN (Online):2051-3305
Published Online:24 September 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Journal of Engineering 2019(20): 6914-6919
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3015260Intelligent RF Sensing for Fall and Health PredictionFrancesco FioranelliEngineering and Physical Sciences Research Council (EPSRC)EP/R041679/1ENG - Systems Power & Energy