IoT of active and healthy ageing: cases from indoor location analytics in the wild
Recently much research has been conducted on early detection of cognitive and physical status deterioration in elderly adults. Primarily the focus is on gait analysis methodologies exploiting average speed, however this presents an issue when used for context aware applications. Additionally data ca...
Gespeichert in:
Veröffentlicht in: | Health and technology 2017-03, Vol.7 (1), p.41-49 |
---|---|
Hauptverfasser: | , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 49 |
---|---|
container_issue | 1 |
container_start_page | 41 |
container_title | Health and technology |
container_volume | 7 |
creator | Konstantinidis, Evdokimos I. Billis, Antonis S. Dupre, Rob Fernández Montenegro, Juan Manuel Conti, Giuseppe Argyriou, Vasileios Bamidis, Panagiotis D. |
description | Recently much research has been conducted on early detection of cognitive and physical status deterioration in elderly adults. Primarily the focus is on gait analysis methodologies exploiting average speed, however this presents an issue when used for context aware applications. Additionally data capture tends to be in short bursts over a long period, allowing for localized temporal factors, such as short term injury, to potentially skew measurements. As such this work collects gait and trajectory IoT data from elderly adults in senior homes (“in the wild”) over a sustained period of time (1 year). Density based clustering algorithms are then applied to the data to provide long-term insights into how the high density regions change over time. The data is collected, analyzed and made available by the indoor analytics client utilizing available processing resources and delivers the analytics outcome even when it is hosted in hardware with constrained resources. Promising results are obtained from the long-term study, suggesting that this form of evaluation has strong potential in the analysis of cognitive and physical status deterioration. |
doi_str_mv | 10.1007/s12553-016-0161-3 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2919465776</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2919465776</sourcerecordid><originalsourceid>FETCH-LOGICAL-c359t-73913b3468ccd1280f34a74937093c6323b14d7feb4176f87b6cc08f97384f7c3</originalsourceid><addsrcrecordid>eNp1kE9LAzEQxYMoWGo_gLeA59XMJptsvEnxT6EgSD2HbDZpU7abmmyVfntTVvTkwDAD83uP4SF0DeQWCBF3CcqqogUBfmoo6BmalCBJIUDy89-9ri_RLKUtyVVBJRmdoLdFWOHgsDaD_7RY9y3eWN0NmyPWa-v79T02OtmEXQw77Ps2hIi7YPTgQ59x3R0Hb1K-4GFj8Zfv2it04XSX7OxnTtH70-Nq_lIsX58X84dlYWglh0JQCbShjNfGtFDWxFGmBZNUEEkNpyVtgLXC2YaB4K4WDTeG1E4KWjMnDJ2im9F3H8PHwaZBbcMh5o-SKiVIxisheKZgpEwMKUXr1D76nY5HBUSd0lNjeiond2pQNGvKUZMy269t_HP-X_QNVmJwFQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2919465776</pqid></control><display><type>article</type><title>IoT of active and healthy ageing: cases from indoor location analytics in the wild</title><source>Springer Nature - Complete Springer Journals</source><source>ProQuest Central</source><creator>Konstantinidis, Evdokimos I. ; Billis, Antonis S. ; Dupre, Rob ; Fernández Montenegro, Juan Manuel ; Conti, Giuseppe ; Argyriou, Vasileios ; Bamidis, Panagiotis D.</creator><creatorcontrib>Konstantinidis, Evdokimos I. ; Billis, Antonis S. ; Dupre, Rob ; Fernández Montenegro, Juan Manuel ; Conti, Giuseppe ; Argyriou, Vasileios ; Bamidis, Panagiotis D.</creatorcontrib><description>Recently much research has been conducted on early detection of cognitive and physical status deterioration in elderly adults. Primarily the focus is on gait analysis methodologies exploiting average speed, however this presents an issue when used for context aware applications. Additionally data capture tends to be in short bursts over a long period, allowing for localized temporal factors, such as short term injury, to potentially skew measurements. As such this work collects gait and trajectory IoT data from elderly adults in senior homes (“in the wild”) over a sustained period of time (1 year). Density based clustering algorithms are then applied to the data to provide long-term insights into how the high density regions change over time. The data is collected, analyzed and made available by the indoor analytics client utilizing available processing resources and delivers the analytics outcome even when it is hosted in hardware with constrained resources. Promising results are obtained from the long-term study, suggesting that this form of evaluation has strong potential in the analysis of cognitive and physical status deterioration.</description><identifier>ISSN: 2190-7188</identifier><identifier>EISSN: 2190-7196</identifier><identifier>DOI: 10.1007/s12553-016-0161-3</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adults ; Algorithms ; Architecture ; Biological and Medical Physics ; Biomedical Engineering and Bioengineering ; Biomedicine ; Biophysics ; Clustering ; Communication ; Computational Biology/Bioinformatics ; Data analysis ; Data capture ; Density ; Engineering ; Gait ; Geriatrics ; Internet of Things ; Localization ; Mathematical analysis ; Medicine/Public Health ; Older people ; Original Paper ; R & D/Technology Policy ; Sensors ; Software ; Systems Medicine</subject><ispartof>Health and technology, 2017-03, Vol.7 (1), p.41-49</ispartof><rights>IUPESM and Springer-Verlag Berlin Heidelberg 2016</rights><rights>IUPESM and Springer-Verlag Berlin Heidelberg 2016.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c359t-73913b3468ccd1280f34a74937093c6323b14d7feb4176f87b6cc08f97384f7c3</citedby><cites>FETCH-LOGICAL-c359t-73913b3468ccd1280f34a74937093c6323b14d7feb4176f87b6cc08f97384f7c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12553-016-0161-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2919465776?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21367,27901,27902,33721,41464,42533,43781,51294</link.rule.ids></links><search><creatorcontrib>Konstantinidis, Evdokimos I.</creatorcontrib><creatorcontrib>Billis, Antonis S.</creatorcontrib><creatorcontrib>Dupre, Rob</creatorcontrib><creatorcontrib>Fernández Montenegro, Juan Manuel</creatorcontrib><creatorcontrib>Conti, Giuseppe</creatorcontrib><creatorcontrib>Argyriou, Vasileios</creatorcontrib><creatorcontrib>Bamidis, Panagiotis D.</creatorcontrib><title>IoT of active and healthy ageing: cases from indoor location analytics in the wild</title><title>Health and technology</title><addtitle>Health Technol</addtitle><description>Recently much research has been conducted on early detection of cognitive and physical status deterioration in elderly adults. Primarily the focus is on gait analysis methodologies exploiting average speed, however this presents an issue when used for context aware applications. Additionally data capture tends to be in short bursts over a long period, allowing for localized temporal factors, such as short term injury, to potentially skew measurements. As such this work collects gait and trajectory IoT data from elderly adults in senior homes (“in the wild”) over a sustained period of time (1 year). Density based clustering algorithms are then applied to the data to provide long-term insights into how the high density regions change over time. The data is collected, analyzed and made available by the indoor analytics client utilizing available processing resources and delivers the analytics outcome even when it is hosted in hardware with constrained resources. Promising results are obtained from the long-term study, suggesting that this form of evaluation has strong potential in the analysis of cognitive and physical status deterioration.</description><subject>Adults</subject><subject>Algorithms</subject><subject>Architecture</subject><subject>Biological and Medical Physics</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Biophysics</subject><subject>Clustering</subject><subject>Communication</subject><subject>Computational Biology/Bioinformatics</subject><subject>Data analysis</subject><subject>Data capture</subject><subject>Density</subject><subject>Engineering</subject><subject>Gait</subject><subject>Geriatrics</subject><subject>Internet of Things</subject><subject>Localization</subject><subject>Mathematical analysis</subject><subject>Medicine/Public Health</subject><subject>Older people</subject><subject>Original Paper</subject><subject>R & D/Technology Policy</subject><subject>Sensors</subject><subject>Software</subject><subject>Systems Medicine</subject><issn>2190-7188</issn><issn>2190-7196</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kE9LAzEQxYMoWGo_gLeA59XMJptsvEnxT6EgSD2HbDZpU7abmmyVfntTVvTkwDAD83uP4SF0DeQWCBF3CcqqogUBfmoo6BmalCBJIUDy89-9ri_RLKUtyVVBJRmdoLdFWOHgsDaD_7RY9y3eWN0NmyPWa-v79T02OtmEXQw77Ps2hIi7YPTgQ59x3R0Hb1K-4GFj8Zfv2it04XSX7OxnTtH70-Nq_lIsX58X84dlYWglh0JQCbShjNfGtFDWxFGmBZNUEEkNpyVtgLXC2YaB4K4WDTeG1E4KWjMnDJ2im9F3H8PHwaZBbcMh5o-SKiVIxisheKZgpEwMKUXr1D76nY5HBUSd0lNjeiond2pQNGvKUZMy269t_HP-X_QNVmJwFQ</recordid><startdate>20170301</startdate><enddate>20170301</enddate><creator>Konstantinidis, Evdokimos I.</creator><creator>Billis, Antonis S.</creator><creator>Dupre, Rob</creator><creator>Fernández Montenegro, Juan Manuel</creator><creator>Conti, Giuseppe</creator><creator>Argyriou, Vasileios</creator><creator>Bamidis, Panagiotis D.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M0T</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PJZUB</scope><scope>PKEHL</scope><scope>PPXIY</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20170301</creationdate><title>IoT of active and healthy ageing: cases from indoor location analytics in the wild</title><author>Konstantinidis, Evdokimos I. ; Billis, Antonis S. ; Dupre, Rob ; Fernández Montenegro, Juan Manuel ; Conti, Giuseppe ; Argyriou, Vasileios ; Bamidis, Panagiotis D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-73913b3468ccd1280f34a74937093c6323b14d7feb4176f87b6cc08f97384f7c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adults</topic><topic>Algorithms</topic><topic>Architecture</topic><topic>Biological and Medical Physics</topic><topic>Biomedical Engineering and Bioengineering</topic><topic>Biomedicine</topic><topic>Biophysics</topic><topic>Clustering</topic><topic>Communication</topic><topic>Computational Biology/Bioinformatics</topic><topic>Data analysis</topic><topic>Data capture</topic><topic>Density</topic><topic>Engineering</topic><topic>Gait</topic><topic>Geriatrics</topic><topic>Internet of Things</topic><topic>Localization</topic><topic>Mathematical analysis</topic><topic>Medicine/Public Health</topic><topic>Older people</topic><topic>Original Paper</topic><topic>R & D/Technology Policy</topic><topic>Sensors</topic><topic>Software</topic><topic>Systems Medicine</topic><toplevel>online_resources</toplevel><creatorcontrib>Konstantinidis, Evdokimos I.</creatorcontrib><creatorcontrib>Billis, Antonis S.</creatorcontrib><creatorcontrib>Dupre, Rob</creatorcontrib><creatorcontrib>Fernández Montenegro, Juan Manuel</creatorcontrib><creatorcontrib>Conti, Giuseppe</creatorcontrib><creatorcontrib>Argyriou, Vasileios</creatorcontrib><creatorcontrib>Bamidis, Panagiotis D.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>ProQuest Health & Medical Research Collection</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Health & Nursing</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied & Life Sciences</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Health and technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Konstantinidis, Evdokimos I.</au><au>Billis, Antonis S.</au><au>Dupre, Rob</au><au>Fernández Montenegro, Juan Manuel</au><au>Conti, Giuseppe</au><au>Argyriou, Vasileios</au><au>Bamidis, Panagiotis D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>IoT of active and healthy ageing: cases from indoor location analytics in the wild</atitle><jtitle>Health and technology</jtitle><stitle>Health Technol</stitle><date>2017-03-01</date><risdate>2017</risdate><volume>7</volume><issue>1</issue><spage>41</spage><epage>49</epage><pages>41-49</pages><issn>2190-7188</issn><eissn>2190-7196</eissn><abstract>Recently much research has been conducted on early detection of cognitive and physical status deterioration in elderly adults. Primarily the focus is on gait analysis methodologies exploiting average speed, however this presents an issue when used for context aware applications. Additionally data capture tends to be in short bursts over a long period, allowing for localized temporal factors, such as short term injury, to potentially skew measurements. As such this work collects gait and trajectory IoT data from elderly adults in senior homes (“in the wild”) over a sustained period of time (1 year). Density based clustering algorithms are then applied to the data to provide long-term insights into how the high density regions change over time. The data is collected, analyzed and made available by the indoor analytics client utilizing available processing resources and delivers the analytics outcome even when it is hosted in hardware with constrained resources. Promising results are obtained from the long-term study, suggesting that this form of evaluation has strong potential in the analysis of cognitive and physical status deterioration.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12553-016-0161-3</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2190-7188 |
ispartof | Health and technology, 2017-03, Vol.7 (1), p.41-49 |
issn | 2190-7188 2190-7196 |
language | eng |
recordid | cdi_proquest_journals_2919465776 |
source | Springer Nature - Complete Springer Journals; ProQuest Central |
subjects | Adults Algorithms Architecture Biological and Medical Physics Biomedical Engineering and Bioengineering Biomedicine Biophysics Clustering Communication Computational Biology/Bioinformatics Data analysis Data capture Density Engineering Gait Geriatrics Internet of Things Localization Mathematical analysis Medicine/Public Health Older people Original Paper R & D/Technology Policy Sensors Software Systems Medicine |
title | IoT of active and healthy ageing: cases from indoor location analytics in the wild |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-20T19%3A53%3A50IST&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=IoT%20of%20active%20and%20healthy%20ageing:%20cases%20from%20indoor%20location%20analytics%20in%20the%20wild&rft.jtitle=Health%20and%20technology&rft.au=Konstantinidis,%20Evdokimos%20I.&rft.date=2017-03-01&rft.volume=7&rft.issue=1&rft.spage=41&rft.epage=49&rft.pages=41-49&rft.issn=2190-7188&rft.eissn=2190-7196&rft_id=info:doi/10.1007/s12553-016-0161-3&rft_dat=%3Cproquest_cross%3E2919465776%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=2919465776&rft_id=info:pmid/&rfr_iscdi=true |