Leveraging Machine Learning for Disease Diagnoses Based on Wearable Devices: A Survey
Many countries around the world are facing a shortage of healthcare resources, especially during the post-epidemic era, leading to a dramatic increase in the need for self-detection and self-management of diseases. The popularity of smart wearable devices, such as smartwatches, and the development o...
Gespeichert in:
Veröffentlicht in: | IEEE internet of things journal 2023-12, Vol.10 (24), p.21959-21981 |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 21981 |
---|---|
container_issue | 24 |
container_start_page | 21959 |
container_title | IEEE internet of things journal |
container_volume | 10 |
creator | Jiang, Zhihan Van Zoest, Vera Deng, Weipeng Ngai, Edith C. H. Liu, Jiangchuan |
description | Many countries around the world are facing a shortage of healthcare resources, especially during the post-epidemic era, leading to a dramatic increase in the need for self-detection and self-management of diseases. The popularity of smart wearable devices, such as smartwatches, and the development of machine learning (ML) bring new opportunities for the early detection and management of various prevalent diseases, such as cardiovascular diseases, Parkinson's disease, and diabetes. In this survey, we comprehensively review the articles related to specific diseases or health issues based on small wearable devices and ML. More specifically, we first present an overview of the articles selected and classify them according to their targeted diseases. Then, we summarize their objectives, wearable device and sensor data, ML techniques, and wearing locations. Based on the literature review, we discuss the challenges and propose future directions from the perspectives of privacy concerns, security concerns, transmission latency and reliability, energy consumption, multimodality, multisensor, multidevices, evaluation metrics, explainability, generalization and personalization, social influence, and human factors, aiming to inspire researchers in this field. |
doi_str_mv | 10.1109/JIOT.2023.3313158 |
format | Article |
fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_2901499676</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10243618</ieee_id><sourcerecordid>2901499676</sourcerecordid><originalsourceid>FETCH-LOGICAL-c434t-f091982c2ba27dbd79308c4d67d8a3e0ce6b1fd4f34860ee237dc65a7a3b8ce53</originalsourceid><addsrcrecordid>eNqNkUtPwkAUhRujiQT5ASYumrgVnDvTTqfuUHxgalgIupxMp7elBFucoRj-vdOUGJau7us7J7k5nncJZARA4tvX6Ww-ooSyEWPAIBQnXo8yGg0DzunpUX_uDaxdEUKcLISY97xFgjs0qiirwn9TellW6CeoTNUu8tr4k9KisuiqKqraovXv3Zj5deV_Ok6la3fDXanR3vlj_70xO9xfeGe5WlscHGrfWzw9zh9ehsnsefowToY6YMF2mJMYYkE1TRWNsjSLYkaEDjIeZUIxJBp5CnkW5CwQnCBSFmWahypSLBUaQ9b3bjpf-4ObJpUbU34ps5e1KuWk_BjL2hSyaWQIQDn9H54vrQQQBBx-3eEbU383aLdyVTemcg9JGhMI4phH3FHQUdrU1hrM_2yByDYe2cYj23jkIR6nueo0JSIe8TRgHAT7BSReixI</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2901499676</pqid></control><display><type>article</type><title>Leveraging Machine Learning for Disease Diagnoses Based on Wearable Devices: A Survey</title><source>IEEE Electronic Library (IEL)</source><creator>Jiang, Zhihan ; Van Zoest, Vera ; Deng, Weipeng ; Ngai, Edith C. H. ; Liu, Jiangchuan</creator><creatorcontrib>Jiang, Zhihan ; Van Zoest, Vera ; Deng, Weipeng ; Ngai, Edith C. H. ; Liu, Jiangchuan</creatorcontrib><description>Many countries around the world are facing a shortage of healthcare resources, especially during the post-epidemic era, leading to a dramatic increase in the need for self-detection and self-management of diseases. The popularity of smart wearable devices, such as smartwatches, and the development of machine learning (ML) bring new opportunities for the early detection and management of various prevalent diseases, such as cardiovascular diseases, Parkinson's disease, and diabetes. In this survey, we comprehensively review the articles related to specific diseases or health issues based on small wearable devices and ML. More specifically, we first present an overview of the articles selected and classify them according to their targeted diseases. Then, we summarize their objectives, wearable device and sensor data, ML techniques, and wearing locations. Based on the literature review, we discuss the challenges and propose future directions from the perspectives of privacy concerns, security concerns, transmission latency and reliability, energy consumption, multimodality, multisensor, multidevices, evaluation metrics, explainability, generalization and personalization, social influence, and human factors, aiming to inspire researchers in this field.</description><identifier>ISSN: 2327-4662</identifier><identifier>EISSN: 2327-4662</identifier><identifier>DOI: 10.1109/JIOT.2023.3313158</identifier><identifier>CODEN: IITJAU</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Computer science ; Deep learning ; Disease diagnoses ; Diseases ; Energy consumption ; Försvarssystem ; Human factors ; Literature reviews ; Machine learning ; machine learning (ML) ; Medical diagnosis ; Medical services ; Parkinson's disease ; physical health ; Smart devices ; smart watches ; Smartwatches ; Surveys ; Systems science for defence and security ; Watches ; Wearable computers ; wearable devices ; Wearable sensors ; Wearable technology</subject><ispartof>IEEE internet of things journal, 2023-12, Vol.10 (24), p.21959-21981</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c434t-f091982c2ba27dbd79308c4d67d8a3e0ce6b1fd4f34860ee237dc65a7a3b8ce53</citedby><cites>FETCH-LOGICAL-c434t-f091982c2ba27dbd79308c4d67d8a3e0ce6b1fd4f34860ee237dc65a7a3b8ce53</cites><orcidid>0000-0002-3017-0874 ; 0000-0002-3454-8731 ; 0000-0003-4857-7143 ; 0000-0001-6592-1984 ; 0000-0002-8861-4001</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10243618$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,780,784,796,885,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10243618$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:fhs:diva-11801$$DView record from Swedish Publication Index$$Hfree_for_read</backlink><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-511262$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Jiang, Zhihan</creatorcontrib><creatorcontrib>Van Zoest, Vera</creatorcontrib><creatorcontrib>Deng, Weipeng</creatorcontrib><creatorcontrib>Ngai, Edith C. H.</creatorcontrib><creatorcontrib>Liu, Jiangchuan</creatorcontrib><title>Leveraging Machine Learning for Disease Diagnoses Based on Wearable Devices: A Survey</title><title>IEEE internet of things journal</title><addtitle>JIoT</addtitle><description>Many countries around the world are facing a shortage of healthcare resources, especially during the post-epidemic era, leading to a dramatic increase in the need for self-detection and self-management of diseases. The popularity of smart wearable devices, such as smartwatches, and the development of machine learning (ML) bring new opportunities for the early detection and management of various prevalent diseases, such as cardiovascular diseases, Parkinson's disease, and diabetes. In this survey, we comprehensively review the articles related to specific diseases or health issues based on small wearable devices and ML. More specifically, we first present an overview of the articles selected and classify them according to their targeted diseases. Then, we summarize their objectives, wearable device and sensor data, ML techniques, and wearing locations. Based on the literature review, we discuss the challenges and propose future directions from the perspectives of privacy concerns, security concerns, transmission latency and reliability, energy consumption, multimodality, multisensor, multidevices, evaluation metrics, explainability, generalization and personalization, social influence, and human factors, aiming to inspire researchers in this field.</description><subject>Computer science</subject><subject>Deep learning</subject><subject>Disease diagnoses</subject><subject>Diseases</subject><subject>Energy consumption</subject><subject>Försvarssystem</subject><subject>Human factors</subject><subject>Literature reviews</subject><subject>Machine learning</subject><subject>machine learning (ML)</subject><subject>Medical diagnosis</subject><subject>Medical services</subject><subject>Parkinson's disease</subject><subject>physical health</subject><subject>Smart devices</subject><subject>smart watches</subject><subject>Smartwatches</subject><subject>Surveys</subject><subject>Systems science for defence and security</subject><subject>Watches</subject><subject>Wearable computers</subject><subject>wearable devices</subject><subject>Wearable sensors</subject><subject>Wearable technology</subject><issn>2327-4662</issn><issn>2327-4662</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqNkUtPwkAUhRujiQT5ASYumrgVnDvTTqfuUHxgalgIupxMp7elBFucoRj-vdOUGJau7us7J7k5nncJZARA4tvX6Ww-ooSyEWPAIBQnXo8yGg0DzunpUX_uDaxdEUKcLISY97xFgjs0qiirwn9TellW6CeoTNUu8tr4k9KisuiqKqraovXv3Zj5deV_Ok6la3fDXanR3vlj_70xO9xfeGe5WlscHGrfWzw9zh9ehsnsefowToY6YMF2mJMYYkE1TRWNsjSLYkaEDjIeZUIxJBp5CnkW5CwQnCBSFmWahypSLBUaQ9b3bjpf-4ObJpUbU34ps5e1KuWk_BjL2hSyaWQIQDn9H54vrQQQBBx-3eEbU383aLdyVTemcg9JGhMI4phH3FHQUdrU1hrM_2yByDYe2cYj23jkIR6nueo0JSIe8TRgHAT7BSReixI</recordid><startdate>20231215</startdate><enddate>20231215</enddate><creator>Jiang, Zhihan</creator><creator>Van Zoest, Vera</creator><creator>Deng, Weipeng</creator><creator>Ngai, Edith C. H.</creator><creator>Liu, Jiangchuan</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>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D8Y</scope><scope>DF2</scope><orcidid>https://orcid.org/0000-0002-3017-0874</orcidid><orcidid>https://orcid.org/0000-0002-3454-8731</orcidid><orcidid>https://orcid.org/0000-0003-4857-7143</orcidid><orcidid>https://orcid.org/0000-0001-6592-1984</orcidid><orcidid>https://orcid.org/0000-0002-8861-4001</orcidid></search><sort><creationdate>20231215</creationdate><title>Leveraging Machine Learning for Disease Diagnoses Based on Wearable Devices: A Survey</title><author>Jiang, Zhihan ; Van Zoest, Vera ; Deng, Weipeng ; Ngai, Edith C. H. ; Liu, Jiangchuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c434t-f091982c2ba27dbd79308c4d67d8a3e0ce6b1fd4f34860ee237dc65a7a3b8ce53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer science</topic><topic>Deep learning</topic><topic>Disease diagnoses</topic><topic>Diseases</topic><topic>Energy consumption</topic><topic>Försvarssystem</topic><topic>Human factors</topic><topic>Literature reviews</topic><topic>Machine learning</topic><topic>machine learning (ML)</topic><topic>Medical diagnosis</topic><topic>Medical services</topic><topic>Parkinson's disease</topic><topic>physical health</topic><topic>Smart devices</topic><topic>smart watches</topic><topic>Smartwatches</topic><topic>Surveys</topic><topic>Systems science for defence and security</topic><topic>Watches</topic><topic>Wearable computers</topic><topic>wearable devices</topic><topic>Wearable sensors</topic><topic>Wearable technology</topic><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Zhihan</creatorcontrib><creatorcontrib>Van Zoest, Vera</creatorcontrib><creatorcontrib>Deng, Weipeng</creatorcontrib><creatorcontrib>Ngai, Edith C. H.</creatorcontrib><creatorcontrib>Liu, Jiangchuan</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>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology 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>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Försvarshögskolan</collection><collection>SWEPUB Uppsala universitet</collection><jtitle>IEEE internet of things journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jiang, Zhihan</au><au>Van Zoest, Vera</au><au>Deng, Weipeng</au><au>Ngai, Edith C. H.</au><au>Liu, Jiangchuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Leveraging Machine Learning for Disease Diagnoses Based on Wearable Devices: A Survey</atitle><jtitle>IEEE internet of things journal</jtitle><stitle>JIoT</stitle><date>2023-12-15</date><risdate>2023</risdate><volume>10</volume><issue>24</issue><spage>21959</spage><epage>21981</epage><pages>21959-21981</pages><issn>2327-4662</issn><eissn>2327-4662</eissn><coden>IITJAU</coden><abstract>Many countries around the world are facing a shortage of healthcare resources, especially during the post-epidemic era, leading to a dramatic increase in the need for self-detection and self-management of diseases. The popularity of smart wearable devices, such as smartwatches, and the development of machine learning (ML) bring new opportunities for the early detection and management of various prevalent diseases, such as cardiovascular diseases, Parkinson's disease, and diabetes. In this survey, we comprehensively review the articles related to specific diseases or health issues based on small wearable devices and ML. More specifically, we first present an overview of the articles selected and classify them according to their targeted diseases. Then, we summarize their objectives, wearable device and sensor data, ML techniques, and wearing locations. Based on the literature review, we discuss the challenges and propose future directions from the perspectives of privacy concerns, security concerns, transmission latency and reliability, energy consumption, multimodality, multisensor, multidevices, evaluation metrics, explainability, generalization and personalization, social influence, and human factors, aiming to inspire researchers in this field.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/JIOT.2023.3313158</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0002-3017-0874</orcidid><orcidid>https://orcid.org/0000-0002-3454-8731</orcidid><orcidid>https://orcid.org/0000-0003-4857-7143</orcidid><orcidid>https://orcid.org/0000-0001-6592-1984</orcidid><orcidid>https://orcid.org/0000-0002-8861-4001</orcidid></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 2327-4662 |
ispartof | IEEE internet of things journal, 2023-12, Vol.10 (24), p.21959-21981 |
issn | 2327-4662 2327-4662 |
language | eng |
recordid | cdi_proquest_journals_2901499676 |
source | IEEE Electronic Library (IEL) |
subjects | Computer science Deep learning Disease diagnoses Diseases Energy consumption Försvarssystem Human factors Literature reviews Machine learning machine learning (ML) Medical diagnosis Medical services Parkinson's disease physical health Smart devices smart watches Smartwatches Surveys Systems science for defence and security Watches Wearable computers wearable devices Wearable sensors Wearable technology |
title | Leveraging Machine Learning for Disease Diagnoses Based on Wearable Devices: A Survey |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T05%3A06%3A42IST&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=Leveraging%20Machine%20Learning%20for%20Disease%20Diagnoses%20Based%20on%20Wearable%20Devices:%20A%20Survey&rft.jtitle=IEEE%20internet%20of%20things%20journal&rft.au=Jiang,%20Zhihan&rft.date=2023-12-15&rft.volume=10&rft.issue=24&rft.spage=21959&rft.epage=21981&rft.pages=21959-21981&rft.issn=2327-4662&rft.eissn=2327-4662&rft.coden=IITJAU&rft_id=info:doi/10.1109/JIOT.2023.3313158&rft_dat=%3Cproquest_RIE%3E2901499676%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=2901499676&rft_id=info:pmid/&rft_ieee_id=10243618&rfr_iscdi=true |