Multi-Hand Gesture Recognition Using Automotive FMCW Radar Sensor

With the development of human–computer interaction(s) (HCI), hand gestures are playing increasingly important roles in our daily lives. With hand gesture recognition (HGR), users can play virtual games together, control the smart equipment, etc. As a result, this paper presents a multi-hand gesture...
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Gespeichert in:

Autor*in:	Yong Wang [verfasserIn] Di Wang [verfasserIn] Yunhai Fu [verfasserIn] Dengke Yao [verfasserIn] Liangbo Xie [verfasserIn] Mu Zhou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	frequency modulated continuous wave radar gesture recognition multi-hand deep learning

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 14(2022), 10, p 2374
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:10, p 2374

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs14102374

Katalog-ID:	DOAJ041933044

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allfields	10.3390/rs14102374 doi (DE-627)DOAJ041933044 (DE-599)DOAJ570bbfd15b6846f3b2f75e283dcdce0a DE-627 ger DE-627 rakwb eng Yong Wang verfasserin aut Multi-Hand Gesture Recognition Using Automotive FMCW Radar Sensor 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the development of human–computer interaction(s) (HCI), hand gestures are playing increasingly important roles in our daily lives. With hand gesture recognition (HGR), users can play virtual games together, control the smart equipment, etc. As a result, this paper presents a multi-hand gesture recognition system using automotive frequency modulated continuous wave (FMCW) radar. Specifically, we first constructed the range-Doppler map (RDM) and range-angle map (RAM), and then suppressed the spectral leakage, and dynamic and static interferences. Since the received echo signals with multi-hand gestures are mixed together, we propose a spatiotemporal path selection algorithm to separate the mixed multi-hand gestures. A dual 3D convolutional neural network-based feature fusion network is proposed for feature extraction and classification. We developed the FMCW radar-based platform to evaluate the performance of the proposed multi-hand gesture recognition method; the experimental results show that the proposed method can achieve an average recognition accuracy of 93.12% when eight gestures with two hands are performed simultaneously. frequency modulated continuous wave radar gesture recognition multi-hand deep learning Science Q Di Wang verfasserin aut Yunhai Fu verfasserin aut Dengke Yao verfasserin aut Liangbo Xie verfasserin aut Mu Zhou verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 10, p 2374 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:10, p 2374 https://doi.org/10.3390/rs14102374 kostenfrei https://doaj.org/article/570bbfd15b6846f3b2f75e283dcdce0a kostenfrei https://www.mdpi.com/2072-4292/14/10/2374 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 10, p 2374
spelling	10.3390/rs14102374 doi (DE-627)DOAJ041933044 (DE-599)DOAJ570bbfd15b6846f3b2f75e283dcdce0a DE-627 ger DE-627 rakwb eng Yong Wang verfasserin aut Multi-Hand Gesture Recognition Using Automotive FMCW Radar Sensor 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the development of human–computer interaction(s) (HCI), hand gestures are playing increasingly important roles in our daily lives. With hand gesture recognition (HGR), users can play virtual games together, control the smart equipment, etc. As a result, this paper presents a multi-hand gesture recognition system using automotive frequency modulated continuous wave (FMCW) radar. Specifically, we first constructed the range-Doppler map (RDM) and range-angle map (RAM), and then suppressed the spectral leakage, and dynamic and static interferences. Since the received echo signals with multi-hand gestures are mixed together, we propose a spatiotemporal path selection algorithm to separate the mixed multi-hand gestures. A dual 3D convolutional neural network-based feature fusion network is proposed for feature extraction and classification. We developed the FMCW radar-based platform to evaluate the performance of the proposed multi-hand gesture recognition method; the experimental results show that the proposed method can achieve an average recognition accuracy of 93.12% when eight gestures with two hands are performed simultaneously. frequency modulated continuous wave radar gesture recognition multi-hand deep learning Science Q Di Wang verfasserin aut Yunhai Fu verfasserin aut Dengke Yao verfasserin aut Liangbo Xie verfasserin aut Mu Zhou verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 10, p 2374 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:10, p 2374 https://doi.org/10.3390/rs14102374 kostenfrei https://doaj.org/article/570bbfd15b6846f3b2f75e283dcdce0a kostenfrei https://www.mdpi.com/2072-4292/14/10/2374 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 10, p 2374
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allfieldsSound	10.3390/rs14102374 doi (DE-627)DOAJ041933044 (DE-599)DOAJ570bbfd15b6846f3b2f75e283dcdce0a DE-627 ger DE-627 rakwb eng Yong Wang verfasserin aut Multi-Hand Gesture Recognition Using Automotive FMCW Radar Sensor 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the development of human–computer interaction(s) (HCI), hand gestures are playing increasingly important roles in our daily lives. With hand gesture recognition (HGR), users can play virtual games together, control the smart equipment, etc. As a result, this paper presents a multi-hand gesture recognition system using automotive frequency modulated continuous wave (FMCW) radar. Specifically, we first constructed the range-Doppler map (RDM) and range-angle map (RAM), and then suppressed the spectral leakage, and dynamic and static interferences. Since the received echo signals with multi-hand gestures are mixed together, we propose a spatiotemporal path selection algorithm to separate the mixed multi-hand gestures. A dual 3D convolutional neural network-based feature fusion network is proposed for feature extraction and classification. We developed the FMCW radar-based platform to evaluate the performance of the proposed multi-hand gesture recognition method; the experimental results show that the proposed method can achieve an average recognition accuracy of 93.12% when eight gestures with two hands are performed simultaneously. frequency modulated continuous wave radar gesture recognition multi-hand deep learning Science Q Di Wang verfasserin aut Yunhai Fu verfasserin aut Dengke Yao verfasserin aut Liangbo Xie verfasserin aut Mu Zhou verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 10, p 2374 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:10, p 2374 https://doi.org/10.3390/rs14102374 kostenfrei https://doaj.org/article/570bbfd15b6846f3b2f75e283dcdce0a kostenfrei https://www.mdpi.com/2072-4292/14/10/2374 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 10, p 2374
language	English
source	In Remote Sensing 14(2022), 10, p 2374 volume:14 year:2022 number:10, p 2374
sourceStr	In Remote Sensing 14(2022), 10, p 2374 volume:14 year:2022 number:10, p 2374
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	frequency modulated continuous wave radar gesture recognition multi-hand deep learning Science Q
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container_title	Remote Sensing
authorswithroles_txt_mv	Yong Wang @@aut@@ Di Wang @@aut@@ Yunhai Fu @@aut@@ Dengke Yao @@aut@@ Liangbo Xie @@aut@@ Mu Zhou @@aut@@
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author	Yong Wang
spellingShingle	Yong Wang misc frequency modulated continuous wave radar misc gesture recognition misc multi-hand misc deep learning misc Science misc Q Multi-Hand Gesture Recognition Using Automotive FMCW Radar Sensor
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topic_title	Multi-Hand Gesture Recognition Using Automotive FMCW Radar Sensor frequency modulated continuous wave radar gesture recognition multi-hand deep learning
topic	misc frequency modulated continuous wave radar misc gesture recognition misc multi-hand misc deep learning misc Science misc Q
topic_unstemmed	misc frequency modulated continuous wave radar misc gesture recognition misc multi-hand misc deep learning misc Science misc Q
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title	Multi-Hand Gesture Recognition Using Automotive FMCW Radar Sensor
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title_sort	multi-hand gesture recognition using automotive fmcw radar sensor
title_auth	Multi-Hand Gesture Recognition Using Automotive FMCW Radar Sensor
abstract	With the development of human–computer interaction(s) (HCI), hand gestures are playing increasingly important roles in our daily lives. With hand gesture recognition (HGR), users can play virtual games together, control the smart equipment, etc. As a result, this paper presents a multi-hand gesture recognition system using automotive frequency modulated continuous wave (FMCW) radar. Specifically, we first constructed the range-Doppler map (RDM) and range-angle map (RAM), and then suppressed the spectral leakage, and dynamic and static interferences. Since the received echo signals with multi-hand gestures are mixed together, we propose a spatiotemporal path selection algorithm to separate the mixed multi-hand gestures. A dual 3D convolutional neural network-based feature fusion network is proposed for feature extraction and classification. We developed the FMCW radar-based platform to evaluate the performance of the proposed multi-hand gesture recognition method; the experimental results show that the proposed method can achieve an average recognition accuracy of 93.12% when eight gestures with two hands are performed simultaneously.
abstractGer	With the development of human–computer interaction(s) (HCI), hand gestures are playing increasingly important roles in our daily lives. With hand gesture recognition (HGR), users can play virtual games together, control the smart equipment, etc. As a result, this paper presents a multi-hand gesture recognition system using automotive frequency modulated continuous wave (FMCW) radar. Specifically, we first constructed the range-Doppler map (RDM) and range-angle map (RAM), and then suppressed the spectral leakage, and dynamic and static interferences. Since the received echo signals with multi-hand gestures are mixed together, we propose a spatiotemporal path selection algorithm to separate the mixed multi-hand gestures. A dual 3D convolutional neural network-based feature fusion network is proposed for feature extraction and classification. We developed the FMCW radar-based platform to evaluate the performance of the proposed multi-hand gesture recognition method; the experimental results show that the proposed method can achieve an average recognition accuracy of 93.12% when eight gestures with two hands are performed simultaneously.
abstract_unstemmed	With the development of human–computer interaction(s) (HCI), hand gestures are playing increasingly important roles in our daily lives. With hand gesture recognition (HGR), users can play virtual games together, control the smart equipment, etc. As a result, this paper presents a multi-hand gesture recognition system using automotive frequency modulated continuous wave (FMCW) radar. Specifically, we first constructed the range-Doppler map (RDM) and range-angle map (RAM), and then suppressed the spectral leakage, and dynamic and static interferences. Since the received echo signals with multi-hand gestures are mixed together, we propose a spatiotemporal path selection algorithm to separate the mixed multi-hand gestures. A dual 3D convolutional neural network-based feature fusion network is proposed for feature extraction and classification. We developed the FMCW radar-based platform to evaluate the performance of the proposed multi-hand gesture recognition method; the experimental results show that the proposed method can achieve an average recognition accuracy of 93.12% when eight gestures with two hands are performed simultaneously.
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title_short	Multi-Hand Gesture Recognition Using Automotive FMCW Radar Sensor
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up_date	2024-07-03T22:51:55.403Z
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