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Content Provider | IET Digital Library |
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Author | Li, Jiayi Shrestha, Aman Kernec, Julien Le Fioranelli, Francesco |
Abstract | 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%. |
Starting Page | 6914 |
Ending Page | 6919 |
Page Count | 6 |
Volume Number | 2019 |
e-ISSN | 20513305 |
Issue Number | Issue 20, Oct (2019) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/20 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0557 |
Journal | The Journal of Engineering |
Publisher | The Institution of Engineering and Technology |
Publisher Date | 2019-07-11 |
Access Restriction | Open |
Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
Subject Keyword | Aperiodic Human Movements Computer Vision And Image Processing Technique Data Augmentation Doppler Radar Feature Extraction Gesture Recognition Hand Gestures Handcrafted Feature Image Classification Image Recognition Image Sampling Image Sonsor Kinect Skeleton Data Knowledge Engineering Technique Man-machine Interaction MicroDoppler Effect Neighbour Classifier Radar Equipment Radar Imaging Remote Sensing Simulated MD Signatures Support Vector Machine Synthetic Minority Oversampling Technique Synthetic Sample System And Application |
Content Type | Text |
Resource Type | Article |
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