Stress detection in daily life scenarios using smart phones and wearable sensors: A survey
[Display omitted] •The types of devices and commonly extracted features for each physiological signal.•The research is divided into laboratory, office, school, automobile and daily life.•Relaxation apps and methods that could be applied to reduce extreme stress.•Research directions and open research...
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Veröffentlicht in: | Journal of biomedical informatics 2019-04, Vol.92, p.103139-103139, Article 103139 |
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container_title | Journal of biomedical informatics |
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creator | Can, Yekta Said Arnrich, Bert Ersoy, Cem |
description | [Display omitted]
•The types of devices and commonly extracted features for each physiological signal.•The research is divided into laboratory, office, school, automobile and daily life.•Relaxation apps and methods that could be applied to reduce extreme stress.•Research directions and open research problems in the topic were identified.
Stress has become a significant cause for many diseases in the modern society. Recently, smartphones, smartwatches and smart wrist-bands have become an integral part of our lives and have reached a widespread usage. This raised the question of whether we can detect and prevent stress with smartphones and wearable sensors. In this survey, we will examine the recent works on stress detection in daily life which are using smartphones and wearable devices. Although there are a number of works related to stress detection in controlled laboratory conditions, the number of studies examining stress detection in daily life is limited. We will divide and investigate the works according to used physiological modality and their targeted environment such as office, campus, car and unrestricted daily life conditions. We will also discuss promising techniques, alleviation methods and research challenges. |
doi_str_mv | 10.1016/j.jbi.2019.103139 |
format | Article |
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•The types of devices and commonly extracted features for each physiological signal.•The research is divided into laboratory, office, school, automobile and daily life.•Relaxation apps and methods that could be applied to reduce extreme stress.•Research directions and open research problems in the topic were identified.
Stress has become a significant cause for many diseases in the modern society. Recently, smartphones, smartwatches and smart wrist-bands have become an integral part of our lives and have reached a widespread usage. This raised the question of whether we can detect and prevent stress with smartphones and wearable sensors. In this survey, we will examine the recent works on stress detection in daily life which are using smartphones and wearable devices. Although there are a number of works related to stress detection in controlled laboratory conditions, the number of studies examining stress detection in daily life is limited. We will divide and investigate the works according to used physiological modality and their targeted environment such as office, campus, car and unrestricted daily life conditions. We will also discuss promising techniques, alleviation methods and research challenges.</description><identifier>ISSN: 1532-0464</identifier><identifier>EISSN: 1532-0480</identifier><identifier>DOI: 10.1016/j.jbi.2019.103139</identifier><identifier>PMID: 30825538</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Daily life physiological data ; Machine learning ; Smartphone ; Stress recognition ; Wearable sensors</subject><ispartof>Journal of biomedical informatics, 2019-04, Vol.92, p.103139-103139, Article 103139</ispartof><rights>2019 Elsevier Inc.</rights><rights>Copyright © 2019 Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c396t-eac1a3465d9fa0811c9852619ced5c8c12dee342dbed2b2d1234b152069ff123</citedby><cites>FETCH-LOGICAL-c396t-eac1a3465d9fa0811c9852619ced5c8c12dee342dbed2b2d1234b152069ff123</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jbi.2019.103139$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30825538$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Can, Yekta Said</creatorcontrib><creatorcontrib>Arnrich, Bert</creatorcontrib><creatorcontrib>Ersoy, Cem</creatorcontrib><title>Stress detection in daily life scenarios using smart phones and wearable sensors: A survey</title><title>Journal of biomedical informatics</title><addtitle>J Biomed Inform</addtitle><description>[Display omitted]
•The types of devices and commonly extracted features for each physiological signal.•The research is divided into laboratory, office, school, automobile and daily life.•Relaxation apps and methods that could be applied to reduce extreme stress.•Research directions and open research problems in the topic were identified.
Stress has become a significant cause for many diseases in the modern society. Recently, smartphones, smartwatches and smart wrist-bands have become an integral part of our lives and have reached a widespread usage. This raised the question of whether we can detect and prevent stress with smartphones and wearable sensors. In this survey, we will examine the recent works on stress detection in daily life which are using smartphones and wearable devices. Although there are a number of works related to stress detection in controlled laboratory conditions, the number of studies examining stress detection in daily life is limited. We will divide and investigate the works according to used physiological modality and their targeted environment such as office, campus, car and unrestricted daily life conditions. We will also discuss promising techniques, alleviation methods and research challenges.</description><subject>Daily life physiological data</subject><subject>Machine learning</subject><subject>Smartphone</subject><subject>Stress recognition</subject><subject>Wearable sensors</subject><issn>1532-0464</issn><issn>1532-0480</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kD1vGzEMhoWiReO4_QFZAo1d7IrS6XJKJyPIR4EAHeqpi6CTeK2Ms84R7xz430eBnYydSAIPX5APYxcgliCg_r5Zbtq4lAJMmRUo84HNQCu5EFUjPr73dXXGzok2QgBoXX9mZ0o0UmvVzNif32NGIh5wRD_GIfGYeHCxP_A-dsjJY3I5DsQniukvp63LI9_9GxISdynwZ3TZtX0hMdGQ6ZqvOE15j4cv7FPnesKvpzpn67vb9c3D4vHX_c-b1ePCK1OPC3QenKpqHUznRAPgTaNlDcZj0L7xIAOiqmRoMchWBpCqakFLUZuuK8OcfTvG7vLwNCGNdhvL1X3vEg4TWQnNVfFgwBQUjqjPA1HGzu5yLA8dLAj7atRubDFqX43ao9Gyc3mKn9othveNN4UF-HEEsPy4j5gt-YipXB9zUWrDEP8T_wJxW4a1</recordid><startdate>201904</startdate><enddate>201904</enddate><creator>Can, Yekta Said</creator><creator>Arnrich, Bert</creator><creator>Ersoy, Cem</creator><general>Elsevier Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>201904</creationdate><title>Stress detection in daily life scenarios using smart phones and wearable sensors: A survey</title><author>Can, Yekta Said ; Arnrich, Bert ; Ersoy, Cem</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c396t-eac1a3465d9fa0811c9852619ced5c8c12dee342dbed2b2d1234b152069ff123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Daily life physiological data</topic><topic>Machine learning</topic><topic>Smartphone</topic><topic>Stress recognition</topic><topic>Wearable sensors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Can, Yekta Said</creatorcontrib><creatorcontrib>Arnrich, Bert</creatorcontrib><creatorcontrib>Ersoy, Cem</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of biomedical informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Can, Yekta Said</au><au>Arnrich, Bert</au><au>Ersoy, Cem</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Stress detection in daily life scenarios using smart phones and wearable sensors: A survey</atitle><jtitle>Journal of biomedical informatics</jtitle><addtitle>J Biomed Inform</addtitle><date>2019-04</date><risdate>2019</risdate><volume>92</volume><spage>103139</spage><epage>103139</epage><pages>103139-103139</pages><artnum>103139</artnum><issn>1532-0464</issn><eissn>1532-0480</eissn><abstract>[Display omitted]
•The types of devices and commonly extracted features for each physiological signal.•The research is divided into laboratory, office, school, automobile and daily life.•Relaxation apps and methods that could be applied to reduce extreme stress.•Research directions and open research problems in the topic were identified.
Stress has become a significant cause for many diseases in the modern society. Recently, smartphones, smartwatches and smart wrist-bands have become an integral part of our lives and have reached a widespread usage. This raised the question of whether we can detect and prevent stress with smartphones and wearable sensors. In this survey, we will examine the recent works on stress detection in daily life which are using smartphones and wearable devices. Although there are a number of works related to stress detection in controlled laboratory conditions, the number of studies examining stress detection in daily life is limited. We will divide and investigate the works according to used physiological modality and their targeted environment such as office, campus, car and unrestricted daily life conditions. We will also discuss promising techniques, alleviation methods and research challenges.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>30825538</pmid><doi>10.1016/j.jbi.2019.103139</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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source | Elsevier ScienceDirect Journals Complete; EZB-FREE-00999 freely available EZB journals |
subjects | Daily life physiological data Machine learning Smartphone Stress recognition Wearable sensors |
title | Stress detection in daily life scenarios using smart phones and wearable sensors: A survey |
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