Advancements in UWB-Based Human Motion Detection Through Wall: A Comprehensive Analysis
Ultra-wide Band (UWB) technology has emerged as a pivotal tool for human motion detection, finding applications in diverse areas ranging from smart homes to automotive safety. This paper presents a comprehensive survey of methodologies employed in UWB-based motion detection, elucidating their streng...
Gespeichert in:
| Veröffentlicht in: | IEEE access 2024, Vol.12, p.89818-89835 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 89835 |
|---|---|
| container_issue | |
| container_start_page | 89818 |
| container_title | IEEE access |
| container_volume | 12 |
| creator | Pardhu, Thottempudi Kumar, Vijay Kumar, Praveen Deevi, Nagesh |
| description | Ultra-wide Band (UWB) technology has emerged as a pivotal tool for human motion detection, finding applications in diverse areas ranging from smart homes to automotive safety. This paper presents a comprehensive survey of methodologies employed in UWB-based motion detection, elucidating their strengths, challenges, and performance metrics. While several methods, including Convolutional Neural Network (CNN) approaches, have been explored, challenges such as motion state overlaps, the necessity for enhanced spatial resolution, and background noise interference persist. Among the various methods analyzed, the SGWO-based RMDL technique emerges as a frontrunner, offering superior accuracy, reduced mean squared error, and impressive true negative and positive rates. Moreover, its computational efficiency sets a precedent in human motion detection. This paper provides insights into the state-of-the-art Through the wall imaging and human vital signs observation for future research and realtime applications. |
| doi_str_mv | 10.1109/ACCESS.2024.3397465 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_ieee_primary_10521532</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10521532</ieee_id><doaj_id>oai_doaj_org_article_e8dba1e13e7f4b1488798cf88d88e341</doaj_id><sourcerecordid>3075415297</sourcerecordid><originalsourceid>FETCH-LOGICAL-c359t-69985c14defacf49f08e933b93333de327260b7d6decc528dd9912435ca7f83f3</originalsourceid><addsrcrecordid>eNpNUV1LJDEQHI47OFF_wd1DwOdZJ-lkktzbOOcXKD6o7GPIJh13ltnJmswK_ntHR8SGpouiq5qmiuIPrRaUVvq0advz-_sFqxhfAGjJa_GjOGC01iUIqH9-w7-L45w31VRqooQ8KJaNf7GDwy0OYybdQB6XZ-WZzejJ1X5rB3Ibxy4O5D-O6D7QwzrF_dOaLG3f_yMNaeN2l3CNQ-5ekDSD7V9zl4-KX8H2GY8_52HxeHH-0F6VN3eX121zUzoQeixrrZVwlHsM1gWuQ6VQA6ymBvAITLK6Wklfe3ROMOW91pRxEM7KoCDAYXE9-_poN2aXuq1NrybaznwQMT0Zm8bO9WhQ-ZWlSAFl4CvKlZJauaCUVwqB08nrZPbapfi8xzyaTdyn6aFsoJKCU8G0nLZg3nIp5pwwfF2llXkPxMyBmPdAzGcgk-rvrOoQ8ZtCMCqAwRsknIX4</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3075415297</pqid></control><display><type>article</type><title>Advancements in UWB-Based Human Motion Detection Through Wall: A Comprehensive Analysis</title><source>Directory of Open Access Journals</source><source>IEEE Xplore Open Access Journals</source><source>EZB Electronic Journals Library</source><creator>Pardhu, Thottempudi ; Kumar, Vijay ; Kumar, Praveen ; Deevi, Nagesh</creator><creatorcontrib>Pardhu, Thottempudi ; Kumar, Vijay ; Kumar, Praveen ; Deevi, Nagesh</creatorcontrib><description>Ultra-wide Band (UWB) technology has emerged as a pivotal tool for human motion detection, finding applications in diverse areas ranging from smart homes to automotive safety. This paper presents a comprehensive survey of methodologies employed in UWB-based motion detection, elucidating their strengths, challenges, and performance metrics. While several methods, including Convolutional Neural Network (CNN) approaches, have been explored, challenges such as motion state overlaps, the necessity for enhanced spatial resolution, and background noise interference persist. Among the various methods analyzed, the SGWO-based RMDL technique emerges as a frontrunner, offering superior accuracy, reduced mean squared error, and impressive true negative and positive rates. Moreover, its computational efficiency sets a precedent in human motion detection. This paper provides insights into the state-of-the-art Through the wall imaging and human vital signs observation for future research and realtime applications.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2024.3397465</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>accuracy ; Artificial neural networks ; Background noise ; Classification algorithms ; CNN ; Convolutional neural networks ; deep learning ; Human activity recognition ; Human motion ; human motion detection ; motion classification ; Motion detection ; Motion perception ; MSE ; Performance measurement ; Radar ; Radar detection ; Radar imaging ; SGWO-based RMDL ; Smart buildings ; Spatial resolution ; Synthetic aperture radar ; TNR ; TPR ; Ultra wideband radar ; Ultra wideband technology ; Ultra-wide band (UWB)</subject><ispartof>IEEE access, 2024, Vol.12, p.89818-89835</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c359t-69985c14defacf49f08e933b93333de327260b7d6decc528dd9912435ca7f83f3</cites><orcidid>0000-0001-8738-8404 ; 0000-0003-1227-3164 ; 0000-0002-9653-1951</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10521532$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,777,781,861,2096,4010,27614,27904,27905,27906,54914</link.rule.ids></links><search><creatorcontrib>Pardhu, Thottempudi</creatorcontrib><creatorcontrib>Kumar, Vijay</creatorcontrib><creatorcontrib>Kumar, Praveen</creatorcontrib><creatorcontrib>Deevi, Nagesh</creatorcontrib><title>Advancements in UWB-Based Human Motion Detection Through Wall: A Comprehensive Analysis</title><title>IEEE access</title><addtitle>Access</addtitle><description>Ultra-wide Band (UWB) technology has emerged as a pivotal tool for human motion detection, finding applications in diverse areas ranging from smart homes to automotive safety. This paper presents a comprehensive survey of methodologies employed in UWB-based motion detection, elucidating their strengths, challenges, and performance metrics. While several methods, including Convolutional Neural Network (CNN) approaches, have been explored, challenges such as motion state overlaps, the necessity for enhanced spatial resolution, and background noise interference persist. Among the various methods analyzed, the SGWO-based RMDL technique emerges as a frontrunner, offering superior accuracy, reduced mean squared error, and impressive true negative and positive rates. Moreover, its computational efficiency sets a precedent in human motion detection. This paper provides insights into the state-of-the-art Through the wall imaging and human vital signs observation for future research and realtime applications.</description><subject>accuracy</subject><subject>Artificial neural networks</subject><subject>Background noise</subject><subject>Classification algorithms</subject><subject>CNN</subject><subject>Convolutional neural networks</subject><subject>deep learning</subject><subject>Human activity recognition</subject><subject>Human motion</subject><subject>human motion detection</subject><subject>motion classification</subject><subject>Motion detection</subject><subject>Motion perception</subject><subject>MSE</subject><subject>Performance measurement</subject><subject>Radar</subject><subject>Radar detection</subject><subject>Radar imaging</subject><subject>SGWO-based RMDL</subject><subject>Smart buildings</subject><subject>Spatial resolution</subject><subject>Synthetic aperture radar</subject><subject>TNR</subject><subject>TPR</subject><subject>Ultra wideband radar</subject><subject>Ultra wideband technology</subject><subject>Ultra-wide band (UWB)</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUV1LJDEQHI47OFF_wd1DwOdZJ-lkktzbOOcXKD6o7GPIJh13ltnJmswK_ntHR8SGpouiq5qmiuIPrRaUVvq0advz-_sFqxhfAGjJa_GjOGC01iUIqH9-w7-L45w31VRqooQ8KJaNf7GDwy0OYybdQB6XZ-WZzejJ1X5rB3Ibxy4O5D-O6D7QwzrF_dOaLG3f_yMNaeN2l3CNQ-5ekDSD7V9zl4-KX8H2GY8_52HxeHH-0F6VN3eX121zUzoQeixrrZVwlHsM1gWuQ6VQA6ymBvAITLK6Wklfe3ROMOW91pRxEM7KoCDAYXE9-_poN2aXuq1NrybaznwQMT0Zm8bO9WhQ-ZWlSAFl4CvKlZJauaCUVwqB08nrZPbapfi8xzyaTdyn6aFsoJKCU8G0nLZg3nIp5pwwfF2llXkPxMyBmPdAzGcgk-rvrOoQ8ZtCMCqAwRsknIX4</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Pardhu, Thottempudi</creator><creator>Kumar, Vijay</creator><creator>Kumar, Praveen</creator><creator>Deevi, Nagesh</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-8738-8404</orcidid><orcidid>https://orcid.org/0000-0003-1227-3164</orcidid><orcidid>https://orcid.org/0000-0002-9653-1951</orcidid></search><sort><creationdate>2024</creationdate><title>Advancements in UWB-Based Human Motion Detection Through Wall: A Comprehensive Analysis</title><author>Pardhu, Thottempudi ; Kumar, Vijay ; Kumar, Praveen ; Deevi, Nagesh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-69985c14defacf49f08e933b93333de327260b7d6decc528dd9912435ca7f83f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>accuracy</topic><topic>Artificial neural networks</topic><topic>Background noise</topic><topic>Classification algorithms</topic><topic>CNN</topic><topic>Convolutional neural networks</topic><topic>deep learning</topic><topic>Human activity recognition</topic><topic>Human motion</topic><topic>human motion detection</topic><topic>motion classification</topic><topic>Motion detection</topic><topic>Motion perception</topic><topic>MSE</topic><topic>Performance measurement</topic><topic>Radar</topic><topic>Radar detection</topic><topic>Radar imaging</topic><topic>SGWO-based RMDL</topic><topic>Smart buildings</topic><topic>Spatial resolution</topic><topic>Synthetic aperture radar</topic><topic>TNR</topic><topic>TPR</topic><topic>Ultra wideband radar</topic><topic>Ultra wideband technology</topic><topic>Ultra-wide band (UWB)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pardhu, Thottempudi</creatorcontrib><creatorcontrib>Kumar, Vijay</creatorcontrib><creatorcontrib>Kumar, Praveen</creatorcontrib><creatorcontrib>Deevi, Nagesh</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE Xplore Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pardhu, Thottempudi</au><au>Kumar, Vijay</au><au>Kumar, Praveen</au><au>Deevi, Nagesh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Advancements in UWB-Based Human Motion Detection Through Wall: A Comprehensive Analysis</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2024</date><risdate>2024</risdate><volume>12</volume><spage>89818</spage><epage>89835</epage><pages>89818-89835</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>Ultra-wide Band (UWB) technology has emerged as a pivotal tool for human motion detection, finding applications in diverse areas ranging from smart homes to automotive safety. This paper presents a comprehensive survey of methodologies employed in UWB-based motion detection, elucidating their strengths, challenges, and performance metrics. While several methods, including Convolutional Neural Network (CNN) approaches, have been explored, challenges such as motion state overlaps, the necessity for enhanced spatial resolution, and background noise interference persist. Among the various methods analyzed, the SGWO-based RMDL technique emerges as a frontrunner, offering superior accuracy, reduced mean squared error, and impressive true negative and positive rates. Moreover, its computational efficiency sets a precedent in human motion detection. This paper provides insights into the state-of-the-art Through the wall imaging and human vital signs observation for future research and realtime applications.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2024.3397465</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0001-8738-8404</orcidid><orcidid>https://orcid.org/0000-0003-1227-3164</orcidid><orcidid>https://orcid.org/0000-0002-9653-1951</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2169-3536 |
| ispartof | IEEE access, 2024, Vol.12, p.89818-89835 |
| issn | 2169-3536 2169-3536 |
| language | eng |
| recordid | cdi_ieee_primary_10521532 |
| source | Directory of Open Access Journals; IEEE Xplore Open Access Journals; EZB Electronic Journals Library |
| subjects | accuracy Artificial neural networks Background noise Classification algorithms CNN Convolutional neural networks deep learning Human activity recognition Human motion human motion detection motion classification Motion detection Motion perception MSE Performance measurement Radar Radar detection Radar imaging SGWO-based RMDL Smart buildings Spatial resolution Synthetic aperture radar TNR TPR Ultra wideband radar Ultra wideband technology Ultra-wide band (UWB) |
| title | Advancements in UWB-Based Human Motion Detection Through Wall: A Comprehensive Analysis |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T14%3A49%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Advancements%20in%20UWB-Based%20Human%20Motion%20Detection%20Through%20Wall:%20A%20Comprehensive%20Analysis&rft.jtitle=IEEE%20access&rft.au=Pardhu,%20Thottempudi&rft.date=2024&rft.volume=12&rft.spage=89818&rft.epage=89835&rft.pages=89818-89835&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2024.3397465&rft_dat=%3Cproquest_ieee_%3E3075415297%3C/proquest_ieee_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3075415297&rft_id=info:pmid/&rft_ieee_id=10521532&rft_doaj_id=oai_doaj_org_article_e8dba1e13e7f4b1488798cf88d88e341&rfr_iscdi=true |