TY  - INPR
PB  - IEEE
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
TI  - Dynamic hand gesture classification based on radar micro-Doppler signatures
T3  - CIE International Conference on Radar (RADAR)
Y1  - 2017/10/04/
UR  - http://doi.org/10.1109/RADAR.2016.8059518
A1  - Zhang, S
A1  - Li, G
A1  - Ritchie, M
A1  - Fioranelli, F
A1  - Griffiths, H
N2  - 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%.
AV  - public
ID  - discovery10046950
KW  - Thumb
KW  - 
Feature extraction
KW  - 
Radar
KW  - 
Time-frequency analysis
KW  - 
Support vector machines
KW  - 
Gesture recognition
CY  - Guangzhou, China
ER  -