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

被引：29

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：

中国国家自然科学基金; 英国工程与自然科学研究理事会;

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

页码：815 / 820

页数：6

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