Smart Seismic Sensing for Indoor Fall Detection, Location, and Notification
This paper presents a novel real-time smart system performing fall detection, location, and notification based on floor vibration data produced by fall downs. Only using floor vibration as the recognition source, the system incorporates a person identification through vibration produced by footsteps...
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Veröffentlicht in: | IEEE journal of biomedical and health informatics 2020-02, Vol.24 (2), p.524-532 |
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creator | Clemente, Jose Li, Fangyu Valero, Maria Song, WenZhan |
description | This paper presents a novel real-time smart system performing fall detection, location, and notification based on floor vibration data produced by fall downs. Only using floor vibration as the recognition source, the system incorporates a person identification through vibration produced by footsteps to inform who is the fallen person. Our approach operates in a real-time style, which means the system recognizes a fall immediately and can identify a person with only one or two footsteps. A collaborative in-network location method is used in which sensors collaborate with each other to recognize the person walking, and more importantly, detect if the person falls down at any moment. We also introduce a voting system among sensor nodes to improve person identification accuracy. Our system is robust to identify fall downs from other possible similar events, such as jumps, door close, and objects fall down. Such a smart system can also be connected to smart commercial devices (such as Google Home or Amazon Alexa) for emergency notifications. Our approach represents an advance in smart technology for elder people who live alone. Evaluation of the system shows that it is able to detect fall downs with an acceptance rate of 95.14% (distinguishing from other possible events), and it identifies people with one or two steps in a 97.22% (higher accuracy than other methods that use more footsteps). The fall down location error is smaller than 0.27 m, which is acceptable compared with the height of a person. |
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Only using floor vibration as the recognition source, the system incorporates a person identification through vibration produced by footsteps to inform who is the fallen person. Our approach operates in a real-time style, which means the system recognizes a fall immediately and can identify a person with only one or two footsteps. A collaborative in-network location method is used in which sensors collaborate with each other to recognize the person walking, and more importantly, detect if the person falls down at any moment. We also introduce a voting system among sensor nodes to improve person identification accuracy. Our system is robust to identify fall downs from other possible similar events, such as jumps, door close, and objects fall down. Such a smart system can also be connected to smart commercial devices (such as Google Home or Amazon Alexa) for emergency notifications. Our approach represents an advance in smart technology for elder people who live alone. Evaluation of the system shows that it is able to detect fall downs with an acceptance rate of 95.14% (distinguishing from other possible events), and it identifies people with one or two steps in a 97.22% (higher accuracy than other methods that use more footsteps). The fall down location error is smaller than 0.27 m, which is acceptable compared with the height of a person.</description><identifier>ISSN: 2168-2194</identifier><identifier>EISSN: 2168-2208</identifier><identifier>DOI: 10.1109/JBHI.2019.2907498</identifier><identifier>PMID: 30946684</identifier><identifier>CODEN: IJBHA9</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Accidental Falls ; Aged ; Collaboration ; Fall detection ; Feature extraction ; Floors ; Humans ; in-network system ; Informatics ; Intelligent sensors ; Monitoring, Ambulatory - instrumentation ; person identification ; Real time ; Real-time systems ; seismic sensing ; Support vector machines ; Vibration ; Vibrations ; Walking</subject><ispartof>IEEE journal of biomedical and health informatics, 2020-02, Vol.24 (2), p.524-532</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c463t-df4199aa54ab1035babed5dfaa203b6ded1d68f359be59d7d087ff7c097fedc33</citedby><cites>FETCH-LOGICAL-c463t-df4199aa54ab1035babed5dfaa203b6ded1d68f359be59d7d087ff7c097fedc33</cites><orcidid>0000-0003-0070-0800 ; 0000-0001-8174-1772 ; 0000-0001-8913-9604 ; 0000-0003-2340-3622</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8678752$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54737</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8678752$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30946684$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Clemente, Jose</creatorcontrib><creatorcontrib>Li, Fangyu</creatorcontrib><creatorcontrib>Valero, Maria</creatorcontrib><creatorcontrib>Song, WenZhan</creatorcontrib><title>Smart Seismic Sensing for Indoor Fall Detection, Location, and Notification</title><title>IEEE journal of biomedical and health informatics</title><addtitle>JBHI</addtitle><addtitle>IEEE J Biomed Health Inform</addtitle><description>This paper presents a novel real-time smart system performing fall detection, location, and notification based on floor vibration data produced by fall downs. Only using floor vibration as the recognition source, the system incorporates a person identification through vibration produced by footsteps to inform who is the fallen person. Our approach operates in a real-time style, which means the system recognizes a fall immediately and can identify a person with only one or two footsteps. A collaborative in-network location method is used in which sensors collaborate with each other to recognize the person walking, and more importantly, detect if the person falls down at any moment. We also introduce a voting system among sensor nodes to improve person identification accuracy. Our system is robust to identify fall downs from other possible similar events, such as jumps, door close, and objects fall down. Such a smart system can also be connected to smart commercial devices (such as Google Home or Amazon Alexa) for emergency notifications. Our approach represents an advance in smart technology for elder people who live alone. Evaluation of the system shows that it is able to detect fall downs with an acceptance rate of 95.14% (distinguishing from other possible events), and it identifies people with one or two steps in a 97.22% (higher accuracy than other methods that use more footsteps). The fall down location error is smaller than 0.27 m, which is acceptable compared with the height of a person.</description><subject>Accidental Falls</subject><subject>Aged</subject><subject>Collaboration</subject><subject>Fall detection</subject><subject>Feature extraction</subject><subject>Floors</subject><subject>Humans</subject><subject>in-network system</subject><subject>Informatics</subject><subject>Intelligent sensors</subject><subject>Monitoring, Ambulatory - instrumentation</subject><subject>person identification</subject><subject>Real time</subject><subject>Real-time systems</subject><subject>seismic sensing</subject><subject>Support vector machines</subject><subject>Vibration</subject><subject>Vibrations</subject><subject>Walking</subject><issn>2168-2194</issn><issn>2168-2208</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpdkD1PwzAQhi0EolXpD0BIKBILAyn-imOPUCgtVDAUZsuJbeQqjUucDPx7XKXtgJf3dH7udHoAuERwghAU96-P88UEQyQmWMCcCn4ChhgxnmIM-emhRoIOwDiENYyPx5Zg52BAoKCMcToEb6uNatpkZVzYuDJmHVz9nVjfJIta-xgzVVXJk2lN2Tpf3yVLX6q-UrVO3n3rrOs7F-DMqiqY8T5H4Gv2_Dmdp8uPl8X0YZmWlJE21ZYiIZTKqCoQJFmhCqMzbZXCkBRMG40045ZkojCZ0LmGPLc2L6HIrdElISNw2-_dNv6nM6GVGxdKU1WqNr4LMgqgjOccioje_EPXvmvqeJ3EJIt2MKZZpFBPlY0PoTFWbhsXvfxKBOVOttzJljvZci87zlzvN3fFxujjxEFtBK56wBljjt-c5TzPMPkDBDaCQA</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Clemente, Jose</creator><creator>Li, Fangyu</creator><creator>Valero, Maria</creator><creator>Song, WenZhan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Li, Fangyu ; Valero, Maria ; Song, WenZhan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c463t-df4199aa54ab1035babed5dfaa203b6ded1d68f359be59d7d087ff7c097fedc33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Accidental Falls</topic><topic>Aged</topic><topic>Collaboration</topic><topic>Fall detection</topic><topic>Feature extraction</topic><topic>Floors</topic><topic>Humans</topic><topic>in-network system</topic><topic>Informatics</topic><topic>Intelligent sensors</topic><topic>Monitoring, Ambulatory - instrumentation</topic><topic>person identification</topic><topic>Real time</topic><topic>Real-time systems</topic><topic>seismic sensing</topic><topic>Support vector machines</topic><topic>Vibration</topic><topic>Vibrations</topic><topic>Walking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Clemente, Jose</creatorcontrib><creatorcontrib>Li, Fangyu</creatorcontrib><creatorcontrib>Valero, Maria</creatorcontrib><creatorcontrib>Song, WenZhan</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>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</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>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE journal of biomedical and health informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Clemente, Jose</au><au>Li, Fangyu</au><au>Valero, Maria</au><au>Song, WenZhan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Smart Seismic Sensing for Indoor Fall Detection, Location, and Notification</atitle><jtitle>IEEE journal of biomedical and health informatics</jtitle><stitle>JBHI</stitle><addtitle>IEEE J Biomed Health Inform</addtitle><date>2020-02-01</date><risdate>2020</risdate><volume>24</volume><issue>2</issue><spage>524</spage><epage>532</epage><pages>524-532</pages><issn>2168-2194</issn><eissn>2168-2208</eissn><coden>IJBHA9</coden><abstract>This paper presents a novel real-time smart system performing fall detection, location, and notification based on floor vibration data produced by fall downs. 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Evaluation of the system shows that it is able to detect fall downs with an acceptance rate of 95.14% (distinguishing from other possible events), and it identifies people with one or two steps in a 97.22% (higher accuracy than other methods that use more footsteps). The fall down location error is smaller than 0.27 m, which is acceptable compared with the height of a person.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>30946684</pmid><doi>10.1109/JBHI.2019.2907498</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-0070-0800</orcidid><orcidid>https://orcid.org/0000-0001-8174-1772</orcidid><orcidid>https://orcid.org/0000-0001-8913-9604</orcidid><orcidid>https://orcid.org/0000-0003-2340-3622</orcidid></addata></record> |
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subjects | Accidental Falls Aged Collaboration Fall detection Feature extraction Floors Humans in-network system Informatics Intelligent sensors Monitoring, Ambulatory - instrumentation person identification Real time Real-time systems seismic sensing Support vector machines Vibration Vibrations Walking |
title | Smart Seismic Sensing for Indoor Fall Detection, Location, and Notification |
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