A Real-Time and Self-Calibrating Algorithm Based on Triaxial Accelerometer Signals for the Detection of Human Posture and Activity
Assessment of human activity and posture with triaxial accelerometers provides insightful information about the functional ability: classification of human activities in rehabilitation and elderly surveillance contexts has been already proposed in the literature. In the meanwhile, recent technologic...
Gespeichert in:
| Veröffentlicht in: | IEEE journal of biomedical and health informatics 2010-07, Vol.14 (4), p.1098-1105 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1105 |
|---|---|
| container_issue | 4 |
| container_start_page | 1098 |
| container_title | IEEE journal of biomedical and health informatics |
| container_volume | 14 |
| creator | Curone, Davide Bertolotti, Gian Mario Cristiani, Andrea Secco, Emanuele Lindo Magenes, Giovanni |
| description | Assessment of human activity and posture with triaxial accelerometers provides insightful information about the functional ability: classification of human activities in rehabilitation and elderly surveillance contexts has been already proposed in the literature. In the meanwhile, recent technological advances allow developing miniaturized wearable devices, integrated within garments, which may extend this assessment to novel tasks, such as real-time remote surveillance of workers and emergency operators intervening in harsh environments. We present an algorithm for human posture and activity-level detection, based on the real-time processing of the signals produced by one wearable triaxial accelerometer. The algorithm is independent of the sensor orientation with respect to the body. Furthermore, it associates to its outputs a "reliability" value, representing the classification quality, in order to launch reliable alarms only when effective dangerous conditions are detected. The system was tested on a customized device to estimate the computational resources needed for real-time functioning. Results exhibit an overall 96.2% accuracy when classifying both static and dynamic activities. |
| doi_str_mv | 10.1109/TITB.2010.2050696 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TITB_2010_2050696</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5466127</ieee_id><sourcerecordid>734030895</sourcerecordid><originalsourceid>FETCH-LOGICAL-c380t-38ce1a4087e1074c4e83a96eae1837396355b1f3bca1236931e55fed59c151143</originalsourceid><addsrcrecordid>eNqFkU9v1DAQxS0EoqXwARASstQDpxSPHf87pgullSqBaDhHXu9k6yqJi50geuWT16tdeuDCyWPP7z2P5hHyFtgZALMf26v2_IyzcuVMMmXVM3IMUpqKMcGfl5oZW2mt4Yi8yvmOMagliJfkiLPaCGXsMfnT0O_ohqoNI1I3begNDn21ckNYJzeHaUubYRtTmG9Heu4ybmicaJuC-x3cQBvvccAUR5wx0ZuwndyQaR8TnW-Rfiqvfg5FEHt6uYxuot9inpe0_6kpvV9hfnhNXvRFhm8O5wn5cfG5XV1W11-_XK2a68oLw-ZKGI_gamY0AtO1r9EIZxU6BCO0sEpIuYZerL0DLpQVgFL2uJHWgwSoxQn5sPe9T_HngnnuxpDL_IObMC65M2CM4FzAf0ktaibKbmUhT_8h7-KSdlvogHFdzKxihYI95VPMOWHf3acwuvRQoG6XZLdLstsl2R2SLJr3B-dlPeLmSfE3ugK82wMBEZ_aslYKuBaPdWSggA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1027231960</pqid></control><display><type>article</type><title>A Real-Time and Self-Calibrating Algorithm Based on Triaxial Accelerometer Signals for the Detection of Human Posture and Activity</title><source>IEEE Electronic Library (IEL)</source><creator>Curone, Davide ; Bertolotti, Gian Mario ; Cristiani, Andrea ; Secco, Emanuele Lindo ; Magenes, Giovanni</creator><creatorcontrib>Curone, Davide ; Bertolotti, Gian Mario ; Cristiani, Andrea ; Secco, Emanuele Lindo ; Magenes, Giovanni</creatorcontrib><description>Assessment of human activity and posture with triaxial accelerometers provides insightful information about the functional ability: classification of human activities in rehabilitation and elderly surveillance contexts has been already proposed in the literature. In the meanwhile, recent technological advances allow developing miniaturized wearable devices, integrated within garments, which may extend this assessment to novel tasks, such as real-time remote surveillance of workers and emergency operators intervening in harsh environments. We present an algorithm for human posture and activity-level detection, based on the real-time processing of the signals produced by one wearable triaxial accelerometer. The algorithm is independent of the sensor orientation with respect to the body. Furthermore, it associates to its outputs a "reliability" value, representing the classification quality, in order to launch reliable alarms only when effective dangerous conditions are detected. The system was tested on a customized device to estimate the computational resources needed for real-time functioning. Results exhibit an overall 96.2% accuracy when classifying both static and dynamic activities.</description><identifier>ISSN: 1089-7771</identifier><identifier>ISSN: 2168-2194</identifier><identifier>EISSN: 1558-0032</identifier><identifier>EISSN: 2168-2208</identifier><identifier>DOI: 10.1109/TITB.2010.2050696</identifier><identifier>PMID: 20483689</identifier><identifier>CODEN: ITIBFX</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Accelerometers ; Activity and posture monitoring ; Algorithms ; Humans ; Posture ; Real time systems ; real-time movement classification ; Senior citizens ; Signal detection ; Signal processing ; Surveillance ; System testing ; triaxial accelerometer ; wearable device ; Wearable sensors</subject><ispartof>IEEE journal of biomedical and health informatics, 2010-07, Vol.14 (4), p.1098-1105</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jul 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c380t-38ce1a4087e1074c4e83a96eae1837396355b1f3bca1236931e55fed59c151143</citedby><cites>FETCH-LOGICAL-c380t-38ce1a4087e1074c4e83a96eae1837396355b1f3bca1236931e55fed59c151143</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5466127$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5466127$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20483689$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Curone, Davide</creatorcontrib><creatorcontrib>Bertolotti, Gian Mario</creatorcontrib><creatorcontrib>Cristiani, Andrea</creatorcontrib><creatorcontrib>Secco, Emanuele Lindo</creatorcontrib><creatorcontrib>Magenes, Giovanni</creatorcontrib><title>A Real-Time and Self-Calibrating Algorithm Based on Triaxial Accelerometer Signals for the Detection of Human Posture and Activity</title><title>IEEE journal of biomedical and health informatics</title><addtitle>TITB</addtitle><addtitle>IEEE Trans Inf Technol Biomed</addtitle><description>Assessment of human activity and posture with triaxial accelerometers provides insightful information about the functional ability: classification of human activities in rehabilitation and elderly surveillance contexts has been already proposed in the literature. In the meanwhile, recent technological advances allow developing miniaturized wearable devices, integrated within garments, which may extend this assessment to novel tasks, such as real-time remote surveillance of workers and emergency operators intervening in harsh environments. We present an algorithm for human posture and activity-level detection, based on the real-time processing of the signals produced by one wearable triaxial accelerometer. The algorithm is independent of the sensor orientation with respect to the body. Furthermore, it associates to its outputs a "reliability" value, representing the classification quality, in order to launch reliable alarms only when effective dangerous conditions are detected. The system was tested on a customized device to estimate the computational resources needed for real-time functioning. Results exhibit an overall 96.2% accuracy when classifying both static and dynamic activities.</description><subject>Accelerometers</subject><subject>Activity and posture monitoring</subject><subject>Algorithms</subject><subject>Humans</subject><subject>Posture</subject><subject>Real time systems</subject><subject>real-time movement classification</subject><subject>Senior citizens</subject><subject>Signal detection</subject><subject>Signal processing</subject><subject>Surveillance</subject><subject>System testing</subject><subject>triaxial accelerometer</subject><subject>wearable device</subject><subject>Wearable sensors</subject><issn>1089-7771</issn><issn>2168-2194</issn><issn>1558-0032</issn><issn>2168-2208</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqFkU9v1DAQxS0EoqXwARASstQDpxSPHf87pgullSqBaDhHXu9k6yqJi50geuWT16tdeuDCyWPP7z2P5hHyFtgZALMf26v2_IyzcuVMMmXVM3IMUpqKMcGfl5oZW2mt4Yi8yvmOMagliJfkiLPaCGXsMfnT0O_ohqoNI1I3begNDn21ckNYJzeHaUubYRtTmG9Heu4ybmicaJuC-x3cQBvvccAUR5wx0ZuwndyQaR8TnW-Rfiqvfg5FEHt6uYxuot9inpe0_6kpvV9hfnhNXvRFhm8O5wn5cfG5XV1W11-_XK2a68oLw-ZKGI_gamY0AtO1r9EIZxU6BCO0sEpIuYZerL0DLpQVgFL2uJHWgwSoxQn5sPe9T_HngnnuxpDL_IObMC65M2CM4FzAf0ktaibKbmUhT_8h7-KSdlvogHFdzKxihYI95VPMOWHf3acwuvRQoG6XZLdLstsl2R2SLJr3B-dlPeLmSfE3ugK82wMBEZ_aslYKuBaPdWSggA</recordid><startdate>201007</startdate><enddate>201007</enddate><creator>Curone, Davide</creator><creator>Bertolotti, Gian Mario</creator><creator>Cristiani, Andrea</creator><creator>Secco, Emanuele Lindo</creator><creator>Magenes, Giovanni</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>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>201007</creationdate><title>A Real-Time and Self-Calibrating Algorithm Based on Triaxial Accelerometer Signals for the Detection of Human Posture and Activity</title><author>Curone, Davide ; Bertolotti, Gian Mario ; Cristiani, Andrea ; Secco, Emanuele Lindo ; Magenes, Giovanni</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c380t-38ce1a4087e1074c4e83a96eae1837396355b1f3bca1236931e55fed59c151143</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Accelerometers</topic><topic>Activity and posture monitoring</topic><topic>Algorithms</topic><topic>Humans</topic><topic>Posture</topic><topic>Real time systems</topic><topic>real-time movement classification</topic><topic>Senior citizens</topic><topic>Signal detection</topic><topic>Signal processing</topic><topic>Surveillance</topic><topic>System testing</topic><topic>triaxial accelerometer</topic><topic>wearable device</topic><topic>Wearable sensors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Curone, Davide</creatorcontrib><creatorcontrib>Bertolotti, Gian Mario</creatorcontrib><creatorcontrib>Cristiani, Andrea</creatorcontrib><creatorcontrib>Secco, Emanuele Lindo</creatorcontrib><creatorcontrib>Magenes, Giovanni</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>Curone, Davide</au><au>Bertolotti, Gian Mario</au><au>Cristiani, Andrea</au><au>Secco, Emanuele Lindo</au><au>Magenes, Giovanni</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Real-Time and Self-Calibrating Algorithm Based on Triaxial Accelerometer Signals for the Detection of Human Posture and Activity</atitle><jtitle>IEEE journal of biomedical and health informatics</jtitle><stitle>TITB</stitle><addtitle>IEEE Trans Inf Technol Biomed</addtitle><date>2010-07</date><risdate>2010</risdate><volume>14</volume><issue>4</issue><spage>1098</spage><epage>1105</epage><pages>1098-1105</pages><issn>1089-7771</issn><issn>2168-2194</issn><eissn>1558-0032</eissn><eissn>2168-2208</eissn><coden>ITIBFX</coden><abstract>Assessment of human activity and posture with triaxial accelerometers provides insightful information about the functional ability: classification of human activities in rehabilitation and elderly surveillance contexts has been already proposed in the literature. In the meanwhile, recent technological advances allow developing miniaturized wearable devices, integrated within garments, which may extend this assessment to novel tasks, such as real-time remote surveillance of workers and emergency operators intervening in harsh environments. We present an algorithm for human posture and activity-level detection, based on the real-time processing of the signals produced by one wearable triaxial accelerometer. The algorithm is independent of the sensor orientation with respect to the body. Furthermore, it associates to its outputs a "reliability" value, representing the classification quality, in order to launch reliable alarms only when effective dangerous conditions are detected. The system was tested on a customized device to estimate the computational resources needed for real-time functioning. Results exhibit an overall 96.2% accuracy when classifying both static and dynamic activities.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>20483689</pmid><doi>10.1109/TITB.2010.2050696</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1089-7771 |
| ispartof | IEEE journal of biomedical and health informatics, 2010-07, Vol.14 (4), p.1098-1105 |
| issn | 1089-7771 2168-2194 1558-0032 2168-2208 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_TITB_2010_2050696 |
| source | IEEE Electronic Library (IEL) |
| subjects | Accelerometers Activity and posture monitoring Algorithms Humans Posture Real time systems real-time movement classification Senior citizens Signal detection Signal processing Surveillance System testing triaxial accelerometer wearable device Wearable sensors |
| title | A Real-Time and Self-Calibrating Algorithm Based on Triaxial Accelerometer Signals for the Detection of Human Posture and Activity |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-13T01%3A51%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Real-Time%20and%20Self-Calibrating%20Algorithm%20Based%20on%20Triaxial%20Accelerometer%20Signals%20for%20the%20Detection%20of%20Human%20Posture%20and%20Activity&rft.jtitle=IEEE%20journal%20of%20biomedical%20and%20health%20informatics&rft.au=Curone,%20Davide&rft.date=2010-07&rft.volume=14&rft.issue=4&rft.spage=1098&rft.epage=1105&rft.pages=1098-1105&rft.issn=1089-7771&rft.eissn=1558-0032&rft.coden=ITIBFX&rft_id=info:doi/10.1109/TITB.2010.2050696&rft_dat=%3Cproquest_RIE%3E734030895%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1027231960&rft_id=info:pmid/20483689&rft_ieee_id=5466127&rfr_iscdi=true |