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

页码：815 / 820

页数：6

共 50 条

[1] Dynamic Hand Gesture Classification Based on Radar Micro-Doppler Signatures
Zhang, Shimeng
Li, Gang
Ritchie, Matthew
Fioranelli, Francesco
Griffiths, Hugh
2016 CIE INTERNATIONAL CONFERENCE ON RADAR (RADAR), 2016,
[2] Sparsity-based Dynamic Hand Gesture Recognition Using Micro-Doppler Signatures
Li, Gang
Zhang, Rui
Ritchie, Matthew
Griffiths, Hugh
2017 IEEE RADAR CONFERENCE (RADARCONF), 2017, : 928 - 931
[3] Dynamic Hand Gesture Classification Based on Multistatic Radar Micro-Doppler Signatures Using Convolutional Neural Network
Chen, Zhaoxi
Li, Gang
Fioranelli, Francesco
Griffiths, Hugh
2019 IEEE RADAR CONFERENCE (RADARCONF), 2019,
[4] Dynamic Hand Gesture Recognition Based on Micro-Doppler Radar Signatures Using Hidden Gauss-Markov Models
Wang, Zetao
Li, Gang
Yang, Le
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2021, 18 (02) : 291 - 295
[5] Time-frequency signatures of micro-Doppler phenomenon for feature extraction
Chen, VC
Lipps, R
WAVELET APPLICATIONS VII, 2000, 4056 : 220 - 226
[6] Multitaper Time-Frequency Reassigned Spectrogram in Micro-Doppler Radar Signal Analysis
Abratkiewicz, Karol
Samczynski, Piotr
2021 SIGNAL PROCESSING SYMPOSIUM (SPSYMPO), 2021, : 1 - 5
[7] Quantitative Evaluation of Channel Micro-Doppler Capacity for MIMO UWB Radar Human Activity Signals Based on Time-Frequency Signatures
Qi, Fugui
Lv, Hao
Wang, Jianqi
Fathy, Aly E.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 58 (09): : 6138 - 6151
[8] Radar-based fall detection based on Doppler time-frequency signatures for assisted living
Wu, Qisong
Zhang, Yimin D.
Tao, Wenbing
Amin, Moeness G.
IET RADAR SONAR AND NAVIGATION, 2015, 9 (02) : 164 - 172
[9] Removing Micro-Doppler Effect in ISAR Imaging by Promoting Joint Sparsity in Range Profile Sequences and the Time-Frequency Domain
Cheng, Di
Pei, Shiqi
Qu, Haiyou
Chen, Chang
Chen, Weidong
IEEE SENSORS JOURNAL, 2021, 21 (21) : 24613 - 24630
[10] Gesture Classification with Handcrafted Micro-Doppler Features using a FMCW Radar
Sun, Yuliang
Fei, Tai
Schliep, Frank
Pohl, Nils
2018 IEEE MTT-S INTERNATIONAL CONFERENCE ON MICROWAVES FOR INTELLIGENT MOBILITY (ICMIM), 2018, : 69 - 72

← 1 2 3 4 5 →