Effect of sparsity-aware time-frequency analysis on dynamic hand gesture classification with radar micro-Doppler signatures

被引：28
|
作者：
Li, Gang [1 ,2 ]
Zhang, Shimeng [1 ]
Fioranelli, Francesco [3 ]
Griffiths, Hugh [4 ]
机构：
[1] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China
[2] Tsinghua Univ Shenzhen, Res Inst, Shenzhen, Peoples R China
[3] Univ Glasgow, Sch Engn, Glasgow, Lanark, Scotland
[4] UCL, Dept Elect & Elect Engn, London, England
来源：
IET RADAR SONAR AND NAVIGATION | 2018年 / 12卷 / 08期
基金：
英国工程与自然科学研究理事会; 中国国家自然科学基金;
关键词：
time-frequency analysis; gesture recognition; image classification; radar imaging; Doppler radar; feature extraction; support vector machines; radar computing; sparse-aware time-frequency analysis; dynamic hand gesture classification; radar microDoppler signature; dynamic hand gesture recognition; human-computer interaction; time-frequency spectrogram extraction; sparsity-driven time-frequency analysis; empirical microDoppler feature; support vector machine; RECOGNITION; FEATURES;
D O I：
10.1049/iet-rsn.2017.0570
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Dynamic hand gesture recognition is of great importance in human-computer interaction. In this study, the authors investigate the effect of sparsity-driven time-frequency analysis on hand gesture classification. The time-frequency spectrogram is first obtained by sparsity-driven time-frequency analysis. Then three empirical micro-Doppler features are extracted from the time-frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time-frequency analysis techniques, sparsity-driven time-frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification.
引用
收藏
页码：815 / 820
页数：6
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