A Framework for Remote Interaction and Management of Home Care Elderly Adults
Growing aging population highlights the importance of managing chronic diseases. The rapid development of Internet of Things (IoT) and big data analysis makes it feasible and affordable to monitor and manage chronic diseases for caring at home. The process of chronic diseases management involves mon...
Gespeichert in:
| Veröffentlicht in: | IEEE sensors journal 2022-06, Vol.22 (11), p.11034-11044 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 11044 |
|---|---|
| container_issue | 11 |
| container_start_page | 11034 |
| container_title | IEEE sensors journal |
| container_volume | 22 |
| creator | Zhang, Bin Zhu, Leqi Pei, Zichen Zhai, Qian Zhu, Junhong Zhong, Xiang Yi, Jingang Liu, Tao |
| description | Growing aging population highlights the importance of managing chronic diseases. The rapid development of Internet of Things (IoT) and big data analysis makes it feasible and affordable to monitor and manage chronic diseases for caring at home. The process of chronic diseases management involves monitoring rehabilitation and recovery, tracking physiological and behavioral status, and health condition classification and diagnosis. In this paper, we proposed a framework for monitoring and management of home care elderly adults. A three-level architecture, which is IoT-Intelligent Terminal(IT)-Cloud, is established to achieve data acquisition, signal transmission, remote interaction and diagnosis. Five diagnosis methods including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K Near Neighbor (KNN), and Back Process Neural Network (BPNN) are implemented to evaluate the risk of suffering from heart disease and experiments showed the accuracy is over 95%. Experimental result reveals that the data flow and remote interaction in this system are effective and primary diagnosis is validated. |
| doi_str_mv | 10.1109/JSEN.2022.3170295 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_9762956</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9762956</ieee_id><sourcerecordid>2672088969</sourcerecordid><originalsourceid>FETCH-LOGICAL-c293t-b0acc50164e6a52f442ef9987160ea76296fe9cdc734deffc63443634d64443e3</originalsourceid><addsrcrecordid>eNo9kEtLAzEUhYMoWKs_QNwEXE_Na5LJspS-pFXwAe5CzNxI68ykZlKk_94ZWtzcexbn3Hv4ELqlZEQp0Q-Pr9OnESOMjThVhOn8DA1onhcZVaI47zUnmeDq4xJdte2WEKpVrgZoPcazaGv4DfEb-xDxC9QhAV42CaJ1aRMabJsSr21jv6CGJuHg8SLUgCc2Ap5WJcTqgMflvkrtNbrwtmrh5rSH6H02fZssstXzfDkZrzLHNE_ZJ7HO5YRKAdLmzAvBwGtdKCoJWCWZlh60K53iogTvneRC8G6UUnQC-BDdH-_uYvjZQ5vMNuxj0700TCpGikJL3bno0eViaNsI3uziprbxYCgxPTXTUzM9NXOi1mXujpkNAPz7dd8pl_wP1jpndw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2672088969</pqid></control><display><type>article</type><title>A Framework for Remote Interaction and Management of Home Care Elderly Adults</title><source>IEEE Xplore</source><creator>Zhang, Bin ; Zhu, Leqi ; Pei, Zichen ; Zhai, Qian ; Zhu, Junhong ; Zhong, Xiang ; Yi, Jingang ; Liu, Tao</creator><creatorcontrib>Zhang, Bin ; Zhu, Leqi ; Pei, Zichen ; Zhai, Qian ; Zhu, Junhong ; Zhong, Xiang ; Yi, Jingang ; Liu, Tao</creatorcontrib><description>Growing aging population highlights the importance of managing chronic diseases. The rapid development of Internet of Things (IoT) and big data analysis makes it feasible and affordable to monitor and manage chronic diseases for caring at home. The process of chronic diseases management involves monitoring rehabilitation and recovery, tracking physiological and behavioral status, and health condition classification and diagnosis. In this paper, we proposed a framework for monitoring and management of home care elderly adults. A three-level architecture, which is IoT-Intelligent Terminal(IT)-Cloud, is established to achieve data acquisition, signal transmission, remote interaction and diagnosis. Five diagnosis methods including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K Near Neighbor (KNN), and Back Process Neural Network (BPNN) are implemented to evaluate the risk of suffering from heart disease and experiments showed the accuracy is over 95%. Experimental result reveals that the data flow and remote interaction in this system are effective and primary diagnosis is validated.</description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2022.3170295</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adults ; Artificial neural networks ; Big Data ; Biomedical monitoring ; Cameras ; Chronic illnesses ; Data acquisition ; Data analysis ; Decision trees ; Diagnosis ; Diseases ; Heart diseases ; Internet of Things ; millimeter wave sensors ; Monitoring ; Neural networks ; Older people ; Radar ; Rehabilitation ; sensor data processing ; Sensor system integration ; sensor system networks ; Sensors ; Signal transmission ; Support vector machines ; Telemedicine ; Wearable sensors</subject><ispartof>IEEE sensors journal, 2022-06, Vol.22 (11), p.11034-11044</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-b0acc50164e6a52f442ef9987160ea76296fe9cdc734deffc63443634d64443e3</citedby><cites>FETCH-LOGICAL-c293t-b0acc50164e6a52f442ef9987160ea76296fe9cdc734deffc63443634d64443e3</cites><orcidid>0000-0002-6214-5876 ; 0000-0002-2797-0264 ; 0000-0003-0628-9098 ; 0000-0002-8612-0745 ; 0000-0002-8217-4930 ; 0000-0001-8426-1205</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9762956$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9762956$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhang, Bin</creatorcontrib><creatorcontrib>Zhu, Leqi</creatorcontrib><creatorcontrib>Pei, Zichen</creatorcontrib><creatorcontrib>Zhai, Qian</creatorcontrib><creatorcontrib>Zhu, Junhong</creatorcontrib><creatorcontrib>Zhong, Xiang</creatorcontrib><creatorcontrib>Yi, Jingang</creatorcontrib><creatorcontrib>Liu, Tao</creatorcontrib><title>A Framework for Remote Interaction and Management of Home Care Elderly Adults</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><description>Growing aging population highlights the importance of managing chronic diseases. The rapid development of Internet of Things (IoT) and big data analysis makes it feasible and affordable to monitor and manage chronic diseases for caring at home. The process of chronic diseases management involves monitoring rehabilitation and recovery, tracking physiological and behavioral status, and health condition classification and diagnosis. In this paper, we proposed a framework for monitoring and management of home care elderly adults. A three-level architecture, which is IoT-Intelligent Terminal(IT)-Cloud, is established to achieve data acquisition, signal transmission, remote interaction and diagnosis. Five diagnosis methods including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K Near Neighbor (KNN), and Back Process Neural Network (BPNN) are implemented to evaluate the risk of suffering from heart disease and experiments showed the accuracy is over 95%. Experimental result reveals that the data flow and remote interaction in this system are effective and primary diagnosis is validated.</description><subject>Adults</subject><subject>Artificial neural networks</subject><subject>Big Data</subject><subject>Biomedical monitoring</subject><subject>Cameras</subject><subject>Chronic illnesses</subject><subject>Data acquisition</subject><subject>Data analysis</subject><subject>Decision trees</subject><subject>Diagnosis</subject><subject>Diseases</subject><subject>Heart diseases</subject><subject>Internet of Things</subject><subject>millimeter wave sensors</subject><subject>Monitoring</subject><subject>Neural networks</subject><subject>Older people</subject><subject>Radar</subject><subject>Rehabilitation</subject><subject>sensor data processing</subject><subject>Sensor system integration</subject><subject>sensor system networks</subject><subject>Sensors</subject><subject>Signal transmission</subject><subject>Support vector machines</subject><subject>Telemedicine</subject><subject>Wearable sensors</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEtLAzEUhYMoWKs_QNwEXE_Na5LJspS-pFXwAe5CzNxI68ykZlKk_94ZWtzcexbn3Hv4ELqlZEQp0Q-Pr9OnESOMjThVhOn8DA1onhcZVaI47zUnmeDq4xJdte2WEKpVrgZoPcazaGv4DfEb-xDxC9QhAV42CaJ1aRMabJsSr21jv6CGJuHg8SLUgCc2Ap5WJcTqgMflvkrtNbrwtmrh5rSH6H02fZssstXzfDkZrzLHNE_ZJ7HO5YRKAdLmzAvBwGtdKCoJWCWZlh60K53iogTvneRC8G6UUnQC-BDdH-_uYvjZQ5vMNuxj0700TCpGikJL3bno0eViaNsI3uziprbxYCgxPTXTUzM9NXOi1mXujpkNAPz7dd8pl_wP1jpndw</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Zhang, Bin</creator><creator>Zhu, Leqi</creator><creator>Pei, Zichen</creator><creator>Zhai, Qian</creator><creator>Zhu, Junhong</creator><creator>Zhong, Xiang</creator><creator>Yi, Jingang</creator><creator>Liu, Tao</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>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-6214-5876</orcidid><orcidid>https://orcid.org/0000-0002-2797-0264</orcidid><orcidid>https://orcid.org/0000-0003-0628-9098</orcidid><orcidid>https://orcid.org/0000-0002-8612-0745</orcidid><orcidid>https://orcid.org/0000-0002-8217-4930</orcidid><orcidid>https://orcid.org/0000-0001-8426-1205</orcidid></search><sort><creationdate>20220601</creationdate><title>A Framework for Remote Interaction and Management of Home Care Elderly Adults</title><author>Zhang, Bin ; Zhu, Leqi ; Pei, Zichen ; Zhai, Qian ; Zhu, Junhong ; Zhong, Xiang ; Yi, Jingang ; Liu, Tao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-b0acc50164e6a52f442ef9987160ea76296fe9cdc734deffc63443634d64443e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adults</topic><topic>Artificial neural networks</topic><topic>Big Data</topic><topic>Biomedical monitoring</topic><topic>Cameras</topic><topic>Chronic illnesses</topic><topic>Data acquisition</topic><topic>Data analysis</topic><topic>Decision trees</topic><topic>Diagnosis</topic><topic>Diseases</topic><topic>Heart diseases</topic><topic>Internet of Things</topic><topic>millimeter wave sensors</topic><topic>Monitoring</topic><topic>Neural networks</topic><topic>Older people</topic><topic>Radar</topic><topic>Rehabilitation</topic><topic>sensor data processing</topic><topic>Sensor system integration</topic><topic>sensor system networks</topic><topic>Sensors</topic><topic>Signal transmission</topic><topic>Support vector machines</topic><topic>Telemedicine</topic><topic>Wearable sensors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Bin</creatorcontrib><creatorcontrib>Zhu, Leqi</creatorcontrib><creatorcontrib>Pei, Zichen</creatorcontrib><creatorcontrib>Zhai, Qian</creatorcontrib><creatorcontrib>Zhu, Junhong</creatorcontrib><creatorcontrib>Zhong, Xiang</creatorcontrib><creatorcontrib>Yi, Jingang</creatorcontrib><creatorcontrib>Liu, Tao</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE sensors journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhang, Bin</au><au>Zhu, Leqi</au><au>Pei, Zichen</au><au>Zhai, Qian</au><au>Zhu, Junhong</au><au>Zhong, Xiang</au><au>Yi, Jingang</au><au>Liu, Tao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Framework for Remote Interaction and Management of Home Care Elderly Adults</atitle><jtitle>IEEE sensors journal</jtitle><stitle>JSEN</stitle><date>2022-06-01</date><risdate>2022</risdate><volume>22</volume><issue>11</issue><spage>11034</spage><epage>11044</epage><pages>11034-11044</pages><issn>1530-437X</issn><eissn>1558-1748</eissn><coden>ISJEAZ</coden><abstract>Growing aging population highlights the importance of managing chronic diseases. The rapid development of Internet of Things (IoT) and big data analysis makes it feasible and affordable to monitor and manage chronic diseases for caring at home. The process of chronic diseases management involves monitoring rehabilitation and recovery, tracking physiological and behavioral status, and health condition classification and diagnosis. In this paper, we proposed a framework for monitoring and management of home care elderly adults. A three-level architecture, which is IoT-Intelligent Terminal(IT)-Cloud, is established to achieve data acquisition, signal transmission, remote interaction and diagnosis. Five diagnosis methods including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K Near Neighbor (KNN), and Back Process Neural Network (BPNN) are implemented to evaluate the risk of suffering from heart disease and experiments showed the accuracy is over 95%. Experimental result reveals that the data flow and remote interaction in this system are effective and primary diagnosis is validated.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSEN.2022.3170295</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-6214-5876</orcidid><orcidid>https://orcid.org/0000-0002-2797-0264</orcidid><orcidid>https://orcid.org/0000-0003-0628-9098</orcidid><orcidid>https://orcid.org/0000-0002-8612-0745</orcidid><orcidid>https://orcid.org/0000-0002-8217-4930</orcidid><orcidid>https://orcid.org/0000-0001-8426-1205</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1530-437X |
| ispartof | IEEE sensors journal, 2022-06, Vol.22 (11), p.11034-11044 |
| issn | 1530-437X 1558-1748 |
| language | eng |
| recordid | cdi_ieee_primary_9762956 |
| source | IEEE Xplore |
| subjects | Adults Artificial neural networks Big Data Biomedical monitoring Cameras Chronic illnesses Data acquisition Data analysis Decision trees Diagnosis Diseases Heart diseases Internet of Things millimeter wave sensors Monitoring Neural networks Older people Radar Rehabilitation sensor data processing Sensor system integration sensor system networks Sensors Signal transmission Support vector machines Telemedicine Wearable sensors |
| title | A Framework for Remote Interaction and Management of Home Care Elderly Adults |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-07T19%3A42%3A59IST&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%20Framework%20for%20Remote%20Interaction%20and%20Management%20of%20Home%20Care%20Elderly%20Adults&rft.jtitle=IEEE%20sensors%20journal&rft.au=Zhang,%20Bin&rft.date=2022-06-01&rft.volume=22&rft.issue=11&rft.spage=11034&rft.epage=11044&rft.pages=11034-11044&rft.issn=1530-437X&rft.eissn=1558-1748&rft.coden=ISJEAZ&rft_id=info:doi/10.1109/JSEN.2022.3170295&rft_dat=%3Cproquest_RIE%3E2672088969%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=2672088969&rft_id=info:pmid/&rft_ieee_id=9762956&rfr_iscdi=true |