Towards real-time automatic stress detection for office workplaces
Descripción del Articulo
Authors would like to thank to Dirk Heylen, head of HMI Lab of University of Twente, for facilitating us the HMI Lab to conduct the experiments and his early feedback. Also, We thank all the participants who took part in our research. This work has been supported by grant 234-2015-FONDECYT (Master P...
| Autores: | , , |
|---|---|
| Formato: | objeto de conferencia |
| Fecha de Publicación: | 2019 |
| Institución: | Consejo Nacional de Ciencia Tecnología e Innovación |
| Repositorio: | CONCYTEC-Institucional |
| Lenguaje: | inglés |
| OAI Identifier: | oai:repositorio.concytec.gob.pe:20.500.12390/809 |
| Enlace del recurso: | https://hdl.handle.net/20.500.12390/809 https://doi.org/10.1007/978-3-030-11680-4_27 |
| Nivel de acceso: | acceso abierto |
| Materia: | Workplace environments Big data Information management Learning systems Physiology Electrodermal activity Emotional trigger Physiological data Statistical approach Stress detection User satisfaction Stresses https://purl.org/pe-repo/ocde/ford#1.02.01 |
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Towards real-time automatic stress detection for office workplaces |
| title |
Towards real-time automatic stress detection for office workplaces |
| spellingShingle |
Towards real-time automatic stress detection for office workplaces Suni Lopez F. Workplace environments Big data Information management Learning systems Physiology Electrodermal activity Emotional trigger Physiological data Statistical approach Stress detection Stress detection User satisfaction User satisfaction Stresses Stresses https://purl.org/pe-repo/ocde/ford#1.02.01 |
| title_short |
Towards real-time automatic stress detection for office workplaces |
| title_full |
Towards real-time automatic stress detection for office workplaces |
| title_fullStr |
Towards real-time automatic stress detection for office workplaces |
| title_full_unstemmed |
Towards real-time automatic stress detection for office workplaces |
| title_sort |
Towards real-time automatic stress detection for office workplaces |
| author |
Suni Lopez F. |
| author_facet |
Suni Lopez F. Condori-Fernandez N. Catala A. |
| author_role |
author |
| author2 |
Condori-Fernandez N. Catala A. |
| author2_role |
author author |
| dc.contributor.author.fl_str_mv |
Suni Lopez F. Condori-Fernandez N. Catala A. |
| dc.subject.none.fl_str_mv |
Workplace environments |
| topic |
Workplace environments Big data Information management Learning systems Physiology Electrodermal activity Emotional trigger Physiological data Statistical approach Stress detection Stress detection User satisfaction User satisfaction Stresses Stresses https://purl.org/pe-repo/ocde/ford#1.02.01 |
| dc.subject.es_PE.fl_str_mv |
Big data Information management Learning systems Physiology Electrodermal activity Emotional trigger Physiological data Statistical approach Stress detection Stress detection User satisfaction User satisfaction Stresses Stresses |
| dc.subject.ocde.none.fl_str_mv |
https://purl.org/pe-repo/ocde/ford#1.02.01 |
| description |
Authors would like to thank to Dirk Heylen, head of HMI Lab of University of Twente, for facilitating us the HMI Lab to conduct the experiments and his early feedback. Also, We thank all the participants who took part in our research. This work has been supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU). Moreover, this work has received financial support from the Spanish Ministry of Economy, Industry and Competitiveness with the Project: TIN2016-78011-C4-1-R; Council of Culture, Education and University Planning with the project ED431G/08, the European Regional Development Fund (ERDF). |
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2019 |
| dc.date.accessioned.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.available.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.issued.fl_str_mv |
2019 |
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info:eu-repo/semantics/conferenceObject |
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conferenceObject |
| dc.identifier.isbn.none.fl_str_mv |
9783030116798 |
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https://hdl.handle.net/20.500.12390/809 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1007/978-3-030-11680-4_27 |
| dc.identifier.scopus.none.fl_str_mv |
2-s2.0-85063522016 |
| identifier_str_mv |
9783030116798 2-s2.0-85063522016 |
| url |
https://hdl.handle.net/20.500.12390/809 https://doi.org/10.1007/978-3-030-11680-4_27 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartof.none.fl_str_mv |
Communications in Computer and Information Science |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
| dc.publisher.none.fl_str_mv |
Springer Verlag |
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Springer Verlag |
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reponame:CONCYTEC-Institucional instname:Consejo Nacional de Ciencia Tecnología e Innovación instacron:CONCYTEC |
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Consejo Nacional de Ciencia Tecnología e Innovación |
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CONCYTEC |
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CONCYTEC |
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CONCYTEC-Institucional |
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CONCYTEC-Institucional |
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Repositorio Institucional CONCYTEC |
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repositorio@concytec.gob.pe |
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1839175651326689280 |
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Publicationrp02078600rp02079600rp02080600Suni Lopez F.Condori-Fernandez N.Catala A.2024-05-30T23:13:38Z2024-05-30T23:13:38Z20199783030116798https://hdl.handle.net/20.500.12390/809https://doi.org/10.1007/978-3-030-11680-4_272-s2.0-85063522016Authors would like to thank to Dirk Heylen, head of HMI Lab of University of Twente, for facilitating us the HMI Lab to conduct the experiments and his early feedback. Also, We thank all the participants who took part in our research. This work has been supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU). Moreover, this work has received financial support from the Spanish Ministry of Economy, Industry and Competitiveness with the Project: TIN2016-78011-C4-1-R; Council of Culture, Education and University Planning with the project ED431G/08, the European Regional Development Fund (ERDF).In recent years, several stress detection methods have been proposed, usually based on machine learning techniques relying on obstructive sensors, which could be uncomfortable or not suitable in many daily situations. Although studies on emotions are emerging and rising in Software Engineering (SE) research, stress has not been yet well investigated in the SE literature despite its negative impact on user satisfaction and stakeholder performance. In this paper, we investigate whether we can reliably implement a stress detector in a single pipeline suitable for real-time processing following an arousal-based statistical approach. It works with physiological data gathered by the E4-wristband, which registers electrodermal activity (EDA). We have conducted an experiment to analyze the output of our stress detector with regard to the self-reported stress in similar conditions to a quiet office workplace environment when users are exposed to different emotional triggers.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengSpringer VerlagCommunications in Computer and Information Scienceinfo:eu-repo/semantics/openAccessWorkplace environmentsBig data-1Information management-1Learning systems-1Physiology-1Electrodermal activity-1Emotional trigger-1Physiological data-1Statistical approach-1Stress detection-1Stress detection-1User satisfaction-1User satisfaction-1Stresses-1Stresses-1https://purl.org/pe-repo/ocde/ford#1.02.01-1Towards real-time automatic stress detection for office workplacesinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/809oai:repositorio.concytec.gob.pe:20.500.12390/8092024-05-30 15:36:09.529http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="91712966-9b20-4d94-a02e-e68719e8c740"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Towards real-time automatic stress detection for office workplaces</Title> <PublishedIn> <Publication> <Title>Communications in Computer and Information Science</Title> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <DOI>https://doi.org/10.1007/978-3-030-11680-4_27</DOI> <SCP-Number>2-s2.0-85063522016</SCP-Number> <ISBN>9783030116798</ISBN> <Authors> <Author> <DisplayName>Suni Lopez F.</DisplayName> <Person id="rp02078" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Condori-Fernandez N.</DisplayName> <Person id="rp02079" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Catala A.</DisplayName> <Person id="rp02080" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Springer Verlag</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Workplace environments</Keyword> <Keyword>Big data</Keyword> <Keyword>Information management</Keyword> <Keyword>Learning systems</Keyword> <Keyword>Physiology</Keyword> <Keyword>Electrodermal activity</Keyword> <Keyword>Emotional trigger</Keyword> <Keyword>Physiological data</Keyword> <Keyword>Statistical approach</Keyword> <Keyword>Stress detection</Keyword> <Keyword>Stress detection</Keyword> <Keyword>User satisfaction</Keyword> <Keyword>User satisfaction</Keyword> <Keyword>Stresses</Keyword> <Keyword>Stresses</Keyword> <Abstract>In recent years, several stress detection methods have been proposed, usually based on machine learning techniques relying on obstructive sensors, which could be uncomfortable or not suitable in many daily situations. Although studies on emotions are emerging and rising in Software Engineering (SE) research, stress has not been yet well investigated in the SE literature despite its negative impact on user satisfaction and stakeholder performance. In this paper, we investigate whether we can reliably implement a stress detector in a single pipeline suitable for real-time processing following an arousal-based statistical approach. It works with physiological data gathered by the E4-wristband, which registers electrodermal activity (EDA). We have conducted an experiment to analyze the output of our stress detector with regard to the self-reported stress in similar conditions to a quiet office workplace environment when users are exposed to different emotional triggers.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1 |
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13.439101 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
