A Millimetre-Wave Radar-Based Fall Detection Method Using Line Kernel Convolutional Neural Network
Fall accidents are significant threats to the health and life of older people. When a millimetre-wave (mmWave) frequency modulated continuous wave (FMCW) radar is used for fall detection, the selected features for further classification can determine the detection performance. In this paper, a line...
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| Veröffentlicht in: | IEEE sensors journal 2020-11, Vol.20 (22), p.13364-13370 |
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| creator | Wang, Bo Guo, Liang Zhang, Hao Guo, Yong-Xin |
| description | Fall accidents are significant threats to the health and life of older people. When a millimetre-wave (mmWave) frequency modulated continuous wave (FMCW) radar is used for fall detection, the selected features for further classification can determine the detection performance. In this paper, a line kernel convolutional neural network (LKCNN) is proposed to process the baseband data directly to detect fall motions. This method utilizes the characteristic of a convolutional neural network (CNN) that it can learn to extract useful features during the training process. A data sample generation method is also proposed to generate multiple samples for the training process by utilizing the multiple receiving channels and sufficiently small pulse repetition time (PRT). The experiment results show that the proposed method can detect fall motions with high accuracy, sensitivity and specificity with fewer network parameters and less computation cost, which is meaningful in realizing an all-time indoor fall detection system. |
| doi_str_mv | 10.1109/JSEN.2020.3006918 |
| format | Article |
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The experiment results show that the proposed method can detect fall motions with high accuracy, sensitivity and specificity with fewer network parameters and less computation cost, which is meaningful in realizing an all-time indoor fall detection system.</description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2020.3006918</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial neural networks ; Baseband ; Chirp ; Continuous radiation ; Convolutional neural network ; data sample generation ; Fall detection ; Feature extraction ; Kernels ; line convolution kernel ; Millimeter waves ; millimetre-wave radar ; Neural networks ; Parameter sensitivity ; Radar ; Radar antennas ; Radar detection ; Sensors ; Training</subject><ispartof>IEEE sensors journal, 2020-11, Vol.20 (22), p.13364-13370</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-f8015efb04df142123070461dcba060b5c15e86676fc48f4bd25c3c0ef1f77d53</citedby><cites>FETCH-LOGICAL-c293t-f8015efb04df142123070461dcba060b5c15e86676fc48f4bd25c3c0ef1f77d53</cites><orcidid>0000-0002-3978-4709 ; 0000-0002-6618-4741 ; 0000-0001-8842-5609 ; 0000-0002-3973-9377</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9133594$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9133594$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wang, Bo</creatorcontrib><creatorcontrib>Guo, Liang</creatorcontrib><creatorcontrib>Zhang, Hao</creatorcontrib><creatorcontrib>Guo, Yong-Xin</creatorcontrib><title>A Millimetre-Wave Radar-Based Fall Detection Method Using Line Kernel Convolutional Neural Network</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><description>Fall accidents are significant threats to the health and life of older people. When a millimetre-wave (mmWave) frequency modulated continuous wave (FMCW) radar is used for fall detection, the selected features for further classification can determine the detection performance. In this paper, a line kernel convolutional neural network (LKCNN) is proposed to process the baseband data directly to detect fall motions. This method utilizes the characteristic of a convolutional neural network (CNN) that it can learn to extract useful features during the training process. A data sample generation method is also proposed to generate multiple samples for the training process by utilizing the multiple receiving channels and sufficiently small pulse repetition time (PRT). The experiment results show that the proposed method can detect fall motions with high accuracy, sensitivity and specificity with fewer network parameters and less computation cost, which is meaningful in realizing an all-time indoor fall detection system.</description><subject>Artificial neural networks</subject><subject>Baseband</subject><subject>Chirp</subject><subject>Continuous radiation</subject><subject>Convolutional neural network</subject><subject>data sample generation</subject><subject>Fall detection</subject><subject>Feature extraction</subject><subject>Kernels</subject><subject>line convolution kernel</subject><subject>Millimeter waves</subject><subject>millimetre-wave radar</subject><subject>Neural networks</subject><subject>Parameter sensitivity</subject><subject>Radar</subject><subject>Radar antennas</subject><subject>Radar detection</subject><subject>Sensors</subject><subject>Training</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMtOwzAQRSMEEqXwAYiNJdYp49iOk2UpLa-2SEAFu8hxxpBikmInRfw9Da1Y3ZHm3NHoBMEphQGlkF7cPY3ngwgiGDCAOKXJXtCjQiQhlTzZ72YGIWfy9TA48n4JQFMpZC_Ih2RWWlt-YuMwfFFrJI-qUC68VB4LMlHWkitsUDdlXZEZNu91QRa-rN7ItKyQ3KOr0JJRXa1r23aQsmSOrfuL5rt2H8fBgVHW48ku-8FiMn4e3YTTh-vb0XAa6ihlTWgSoAJNDrwwlEc0YiCBx7TQuYIYcqE36ySOZWw0TwzPi0hopgENNVIWgvWD8-3dlau_WvRNtqxbt_nHZxEXNEkTwfmGoltKu9p7hyZbufJTuZ-MQtapzDqVWacy26ncdM62nRIR__mUMiZSzn4BOmhvUQ</recordid><startdate>20201115</startdate><enddate>20201115</enddate><creator>Wang, Bo</creator><creator>Guo, Liang</creator><creator>Zhang, Hao</creator><creator>Guo, Yong-Xin</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-3978-4709</orcidid><orcidid>https://orcid.org/0000-0002-6618-4741</orcidid><orcidid>https://orcid.org/0000-0001-8842-5609</orcidid><orcidid>https://orcid.org/0000-0002-3973-9377</orcidid></search><sort><creationdate>20201115</creationdate><title>A Millimetre-Wave Radar-Based Fall Detection Method Using Line Kernel Convolutional Neural Network</title><author>Wang, Bo ; Guo, Liang ; Zhang, Hao ; Guo, Yong-Xin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-f8015efb04df142123070461dcba060b5c15e86676fc48f4bd25c3c0ef1f77d53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial neural networks</topic><topic>Baseband</topic><topic>Chirp</topic><topic>Continuous radiation</topic><topic>Convolutional neural network</topic><topic>data sample generation</topic><topic>Fall detection</topic><topic>Feature extraction</topic><topic>Kernels</topic><topic>line convolution kernel</topic><topic>Millimeter waves</topic><topic>millimetre-wave radar</topic><topic>Neural networks</topic><topic>Parameter sensitivity</topic><topic>Radar</topic><topic>Radar antennas</topic><topic>Radar detection</topic><topic>Sensors</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Bo</creatorcontrib><creatorcontrib>Guo, Liang</creatorcontrib><creatorcontrib>Zhang, Hao</creatorcontrib><creatorcontrib>Guo, Yong-Xin</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE sensors journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Bo</au><au>Guo, Liang</au><au>Zhang, Hao</au><au>Guo, Yong-Xin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Millimetre-Wave Radar-Based Fall Detection Method Using Line Kernel Convolutional Neural Network</atitle><jtitle>IEEE sensors journal</jtitle><stitle>JSEN</stitle><date>2020-11-15</date><risdate>2020</risdate><volume>20</volume><issue>22</issue><spage>13364</spage><epage>13370</epage><pages>13364-13370</pages><issn>1530-437X</issn><eissn>1558-1748</eissn><coden>ISJEAZ</coden><abstract>Fall accidents are significant threats to the health and life of older people. When a millimetre-wave (mmWave) frequency modulated continuous wave (FMCW) radar is used for fall detection, the selected features for further classification can determine the detection performance. In this paper, a line kernel convolutional neural network (LKCNN) is proposed to process the baseband data directly to detect fall motions. This method utilizes the characteristic of a convolutional neural network (CNN) that it can learn to extract useful features during the training process. A data sample generation method is also proposed to generate multiple samples for the training process by utilizing the multiple receiving channels and sufficiently small pulse repetition time (PRT). 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| subjects | Artificial neural networks Baseband Chirp Continuous radiation Convolutional neural network data sample generation Fall detection Feature extraction Kernels line convolution kernel Millimeter waves millimetre-wave radar Neural networks Parameter sensitivity Radar Radar antennas Radar detection Sensors Training |
| title | A Millimetre-Wave Radar-Based Fall Detection Method Using Line Kernel Convolutional Neural Network |
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