Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications
It is well known that blink, yawn, and heart rate changes give clue about a human's mental state, such as drowsiness and fatigue. In this paper, image sequences, as the raw data, are captured from smart phones which serve as non-contact optical sensors. Video streams containing subject's f...
Gespeichert in:
| Veröffentlicht in: | IEEE access 2019, Vol.7, p.11829-11843 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 11843 |
|---|---|
| container_issue | |
| container_start_page | 11829 |
| container_title | IEEE access |
| container_volume | 7 |
| creator | Zhang, Chao Wu, Xiaopei Zheng, Xi Yu, Shui |
| description | It is well known that blink, yawn, and heart rate changes give clue about a human's mental state, such as drowsiness and fatigue. In this paper, image sequences, as the raw data, are captured from smart phones which serve as non-contact optical sensors. Video streams containing subject's facial region are analyzed to identify the physiological sources that are mixed in each image. We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations. An overall decision is made by analyzing the separated source signals in parallel to determine the driver's driving state. The robustness of the proposed method is tested under various illumination contexts and a variety of head motion modes. Experiments on 15 subjects show that the multi-channel SOBI presents a promising framework to accurately detect drowsiness by merging multi-physiological information in a less complex way. |
| doi_str_mv | 10.1109/ACCESS.2019.2891971 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1109_ACCESS_2019_2891971</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8607970</ieee_id><doaj_id>oai_doaj_org_article_bcb21269a0994610a5bf498f312c8f95</doaj_id><sourcerecordid>2455610831</sourcerecordid><originalsourceid>FETCH-LOGICAL-c474t-8837b14af00f8e3557ffa1192f0bcd8d9d638e1d3f42142526a8678c3794df913</originalsourceid><addsrcrecordid>eNpNkVtLJDEQhZtFYUX9BfPS4HOPqVw6yaP26O6AIjjrc0jnMpuh7WjSo-y_34wtYr1UcajvVMGpqgWgJQCSl1ddd7PZLDECucRCguTwozrB0MqGMNIefZt_Vuc571ApUSTGT6rHVQpvLtWrFN9zGF3O9cpNzkwhjvVTUbb1_X6YQtP91ePohnrjTBxt_ZBsoa6HUOa1deMUfDD6QOWz6tjrIbvzz35aPd3e_Ol-N3cPv9bd1V1jKKdTIwThPVDtEfLCEca49xpAYo96Y4WVtiXCgSWeYqCY4VaLlgtDuKTWSyCn1Xr2tVHv1EsKzzr9U1EH9SHEtFU6TcEMTvWmx4BbqZGUtAWkWe-pFJ4ANsJLVrwuZq-XFF_3Lk9qF_dpLO8rTBkriCCHi2TeMinmnJz_ugpIHbJQcxbqkIX6zKJQi5kKzrkvQrSIS47Ifw1Dg9M</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2455610831</pqid></control><display><type>article</type><title>Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Zhang, Chao ; Wu, Xiaopei ; Zheng, Xi ; Yu, Shui</creator><creatorcontrib>Zhang, Chao ; Wu, Xiaopei ; Zheng, Xi ; Yu, Shui</creatorcontrib><description>It is well known that blink, yawn, and heart rate changes give clue about a human's mental state, such as drowsiness and fatigue. In this paper, image sequences, as the raw data, are captured from smart phones which serve as non-contact optical sensors. Video streams containing subject's facial region are analyzed to identify the physiological sources that are mixed in each image. We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations. An overall decision is made by analyzing the separated source signals in parallel to determine the driver's driving state. The robustness of the proposed method is tested under various illumination contexts and a variety of head motion modes. Experiments on 15 subjects show that the multi-channel SOBI presents a promising framework to accurately detect drowsiness by merging multi-physiological information in a less complex way.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2019.2891971</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Biomedical monitoring ; blink ; Blood volume ; blood volume pulse (BVP) ; Complexity theory ; Decision analysis ; Driver fatigue ; drowsiness detection ; Head movement ; Heart rate ; Heuristic algorithms ; Optical measuring instruments ; Physiology ; second-order blind identification (SOBI) ; Signal processing algorithms ; Vehicles ; Video data ; Yawn</subject><ispartof>IEEE access, 2019, Vol.7, p.11829-11843</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-8837b14af00f8e3557ffa1192f0bcd8d9d638e1d3f42142526a8678c3794df913</citedby><cites>FETCH-LOGICAL-c474t-8837b14af00f8e3557ffa1192f0bcd8d9d638e1d3f42142526a8678c3794df913</cites><orcidid>0000-0002-1100-5566</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8607970$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,4010,27610,27900,27901,27902,54908</link.rule.ids></links><search><creatorcontrib>Zhang, Chao</creatorcontrib><creatorcontrib>Wu, Xiaopei</creatorcontrib><creatorcontrib>Zheng, Xi</creatorcontrib><creatorcontrib>Yu, Shui</creatorcontrib><title>Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications</title><title>IEEE access</title><addtitle>Access</addtitle><description>It is well known that blink, yawn, and heart rate changes give clue about a human's mental state, such as drowsiness and fatigue. In this paper, image sequences, as the raw data, are captured from smart phones which serve as non-contact optical sensors. Video streams containing subject's facial region are analyzed to identify the physiological sources that are mixed in each image. We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations. An overall decision is made by analyzing the separated source signals in parallel to determine the driver's driving state. The robustness of the proposed method is tested under various illumination contexts and a variety of head motion modes. Experiments on 15 subjects show that the multi-channel SOBI presents a promising framework to accurately detect drowsiness by merging multi-physiological information in a less complex way.</description><subject>Biomedical monitoring</subject><subject>blink</subject><subject>Blood volume</subject><subject>blood volume pulse (BVP)</subject><subject>Complexity theory</subject><subject>Decision analysis</subject><subject>Driver fatigue</subject><subject>drowsiness detection</subject><subject>Head movement</subject><subject>Heart rate</subject><subject>Heuristic algorithms</subject><subject>Optical measuring instruments</subject><subject>Physiology</subject><subject>second-order blind identification (SOBI)</subject><subject>Signal processing algorithms</subject><subject>Vehicles</subject><subject>Video data</subject><subject>Yawn</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkVtLJDEQhZtFYUX9BfPS4HOPqVw6yaP26O6AIjjrc0jnMpuh7WjSo-y_34wtYr1UcajvVMGpqgWgJQCSl1ddd7PZLDECucRCguTwozrB0MqGMNIefZt_Vuc571ApUSTGT6rHVQpvLtWrFN9zGF3O9cpNzkwhjvVTUbb1_X6YQtP91ePohnrjTBxt_ZBsoa6HUOa1deMUfDD6QOWz6tjrIbvzz35aPd3e_Ol-N3cPv9bd1V1jKKdTIwThPVDtEfLCEca49xpAYo96Y4WVtiXCgSWeYqCY4VaLlgtDuKTWSyCn1Xr2tVHv1EsKzzr9U1EH9SHEtFU6TcEMTvWmx4BbqZGUtAWkWe-pFJ4ANsJLVrwuZq-XFF_3Lk9qF_dpLO8rTBkriCCHi2TeMinmnJz_ugpIHbJQcxbqkIX6zKJQi5kKzrkvQrSIS47Ifw1Dg9M</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Zhang, Chao</creator><creator>Wu, Xiaopei</creator><creator>Zheng, Xi</creator><creator>Yu, Shui</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/0000-0002-1100-5566</orcidid></search><sort><creationdate>2019</creationdate><title>Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications</title><author>Zhang, Chao ; Wu, Xiaopei ; Zheng, Xi ; Yu, Shui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-8837b14af00f8e3557ffa1192f0bcd8d9d638e1d3f42142526a8678c3794df913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Biomedical monitoring</topic><topic>blink</topic><topic>Blood volume</topic><topic>blood volume pulse (BVP)</topic><topic>Complexity theory</topic><topic>Decision analysis</topic><topic>Driver fatigue</topic><topic>drowsiness detection</topic><topic>Head movement</topic><topic>Heart rate</topic><topic>Heuristic algorithms</topic><topic>Optical measuring instruments</topic><topic>Physiology</topic><topic>second-order blind identification (SOBI)</topic><topic>Signal processing algorithms</topic><topic>Vehicles</topic><topic>Video data</topic><topic>Yawn</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Chao</creatorcontrib><creatorcontrib>Wu, Xiaopei</creatorcontrib><creatorcontrib>Zheng, Xi</creatorcontrib><creatorcontrib>Yu, Shui</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE 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>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Chao</au><au>Wu, Xiaopei</au><au>Zheng, Xi</au><au>Yu, Shui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2019</date><risdate>2019</risdate><volume>7</volume><spage>11829</spage><epage>11843</epage><pages>11829-11843</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>It is well known that blink, yawn, and heart rate changes give clue about a human's mental state, such as drowsiness and fatigue. In this paper, image sequences, as the raw data, are captured from smart phones which serve as non-contact optical sensors. Video streams containing subject's facial region are analyzed to identify the physiological sources that are mixed in each image. We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations. An overall decision is made by analyzing the separated source signals in parallel to determine the driver's driving state. The robustness of the proposed method is tested under various illumination contexts and a variety of head motion modes. Experiments on 15 subjects show that the multi-channel SOBI presents a promising framework to accurately detect drowsiness by merging multi-physiological information in a less complex way.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2019.2891971</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-1100-5566</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2169-3536 |
| ispartof | IEEE access, 2019, Vol.7, p.11829-11843 |
| issn | 2169-3536 2169-3536 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_ACCESS_2019_2891971 |
| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Biomedical monitoring blink Blood volume blood volume pulse (BVP) Complexity theory Decision analysis Driver fatigue drowsiness detection Head movement Heart rate Heuristic algorithms Optical measuring instruments Physiology second-order blind identification (SOBI) Signal processing algorithms Vehicles Video data Yawn |
| title | Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T07%3A10%3A31IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Driver%20Drowsiness%20Detection%20Using%20Multi-Channel%20Second%20Order%20Blind%20Identifications&rft.jtitle=IEEE%20access&rft.au=Zhang,%20Chao&rft.date=2019&rft.volume=7&rft.spage=11829&rft.epage=11843&rft.pages=11829-11843&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2019.2891971&rft_dat=%3Cproquest_cross%3E2455610831%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2455610831&rft_id=info:pmid/&rft_ieee_id=8607970&rft_doaj_id=oai_doaj_org_article_bcb21269a0994610a5bf498f312c8f95&rfr_iscdi=true |