Zhang, S;
Li, G;
Ritchie, M;
Fioranelli, F;
Griffiths, H;
(2017)
Dynamic hand gesture classification based on radar micro-Doppler signatures.
In:
Proceedings of 2016 CIE International Conference on Radar (RADAR 2016).
IEEE: Guangzhou, China.
(In press).
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Abstract
Dynamic hand gesture recognition is of great importance for human-computer interaction. In this paper, we present a method to discriminate the four kinds of dynamic hand gestures, snapping fingers, flipping fingers, hand rotation and calling, using a radar micro-Doppler sensor. Two micro-Doppler features are extracted from the time-frequency spectrum and the support vector machine is used to classify these four kinds of gestures. The experimental results on measured data demonstrate that the proposed method can produce a classification accuracy higher than 88.56%.
| Type: | Proceedings paper |
|---|---|
| Title: | Dynamic hand gesture classification based on radar micro-Doppler signatures |
| Event: | 2016 CIE International Conference on Radar (RADAR 2016) |
| ISBN-13: | 9781509048281 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/RADAR.2016.8059518 |
| Publisher version: | http://doi.org/10.1109/RADAR.2016.8059518 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Thumb, Feature extraction, Radar, Time-frequency analysis, Support vector machines, Gesture recognition |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10046950 |
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