Using Activity-Related Behavioural Features towards More Effective Automatic Stress Detection
Identifieur interne : 000520 ( Pmc/Curation ); précédent : 000519; suivant : 000521Using Activity-Related Behavioural Features towards More Effective Automatic Stress Detection
Auteurs : Dimitris Giakoumis [Grèce] ; Anastasios Drosou [Royaume-Uni] ; Pietro Cipresso [Italie] ; Dimitrios Tzovaras [Grèce] ; George Hassapis [Grèce] ; Andrea Gaggioli [Italie] ; Giuseppe Riva [Italie]Source :
- PLoS ONE [ 1932-6203 ] ; 2012.
Abstract
This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing.
Url:
DOI: 10.1371/journal.pone.0043571
PubMed: 23028461
PubMed Central: 3446965
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and Telematics Institute, Centre for Research and Technology Hellas, Thermi, Thessaloniki</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece</addr-line></nlm:aff><country xml:lang="fr">Grèce</country><wicri:regionArea>Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki</wicri:regionArea></affiliation></author><author><name sortKey="Drosou, Anastasios" sort="Drosou, Anastasios" uniqKey="Drosou A" first="Anastasios" last="Drosou">Anastasios Drosou</name><affiliation wicri:level="1"><nlm:aff id="aff3"><addr-line>Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Department of Electrical and Electronic Engineering, Imperial College London, London</wicri:regionArea></affiliation></author><author><name sortKey="Cipresso, Pietro" sort="Cipresso, Pietro" uniqKey="Cipresso P" first="Pietro" last="Cipresso">Pietro Cipresso</name><affiliation wicri:level="1"><nlm:aff id="aff4"><addr-line>Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy</addr-line></nlm:aff><country xml:lang="fr">Italie</country><wicri:regionArea>Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan</wicri:regionArea></affiliation></author><author><name sortKey="Tzovaras, Dimitrios" sort="Tzovaras, Dimitrios" uniqKey="Tzovaras D" first="Dimitrios" last="Tzovaras">Dimitrios Tzovaras</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Informatics and Telematics Institute, Centre for Research and Technology Hellas, Thermi, Thessaloniki, Greece</addr-line></nlm:aff><country xml:lang="fr">Grèce</country><wicri:regionArea>Informatics and Telematics Institute, Centre for Research and Technology Hellas, Thermi, Thessaloniki</wicri:regionArea></affiliation></author><author><name sortKey="Hassapis, George" sort="Hassapis, George" uniqKey="Hassapis G" first="George" last="Hassapis">George Hassapis</name><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece</addr-line></nlm:aff><country xml:lang="fr">Grèce</country><wicri:regionArea>Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki</wicri:regionArea></affiliation></author><author><name sortKey="Gaggioli, Andrea" sort="Gaggioli, Andrea" uniqKey="Gaggioli A" first="Andrea" last="Gaggioli">Andrea Gaggioli</name><affiliation wicri:level="1"><nlm:aff id="aff4"><addr-line>Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy</addr-line></nlm:aff><country xml:lang="fr">Italie</country><wicri:regionArea>Applied 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Milan, Milan, Italy</addr-line></nlm:aff><country xml:lang="fr">Italie</country><wicri:regionArea>Psychology Department, Catholic University of Milan, Milan</wicri:regionArea></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Hickey, L" uniqKey="Hickey L">L Hickey</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Gaggioli, A" uniqKey="Gaggioli A">A Gaggioli</name></author><author><name sortKey="Pioggia, G" uniqKey="Pioggia G">G Pioggia</name></author><author><name sortKey="Tartarisco, G" uniqKey="Tartarisco G">G Tartarisco</name></author><author><name sortKey="Baldus, G" uniqKey="Baldus G">G Baldus</name></author><author><name sortKey="Corda, D" uniqKey="Corda D">D Corda</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cohen, S" uniqKey="Cohen S">S Cohen</name></author><author><name sortKey="Janicki 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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id><journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosone</journal-id><journal-title-group><journal-title>PLoS ONE</journal-title></journal-title-group><issn pub-type="epub">1932-6203</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">23028461</article-id><article-id pub-id-type="pmc">3446965</article-id><article-id pub-id-type="publisher-id">PONE-D-11-24674</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0043571</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Engineering</subject><subj-group><subject>Human Factors Engineering</subject><subj-group><subject>Man Computer Interface</subject></subj-group></subj-group><subj-group><subject>Signal Processing</subject><subj-group><subject>Video Processing</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Medicine</subject><subj-group><subject>Mental Health</subject><subj-group><subject>Psychology</subject><subj-group><subject>Behavior</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Social and Behavioral Sciences</subject><subj-group><subject>Psychology</subject><subj-group><subject>Therapies</subject><subj-group><subject>Psychotherapy</subject></subj-group></subj-group><subj-group><subject>Behavior</subject></subj-group></subj-group></subj-group></article-categories><title-group><article-title>Using Activity-Related Behavioural Features towards More Effective Automatic Stress Detection</article-title><alt-title alt-title-type="running-head">Behavioural Features for Stress Detection</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Giakoumis</surname><given-names>Dimitris</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Drosou</surname><given-names>Anastasios</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author"><name><surname>Cipresso</surname><given-names>Pietro</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref></contrib><contrib contrib-type="author"><name><surname>Tzovaras</surname><given-names>Dimitrios</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Hassapis</surname><given-names>George</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Gaggioli</surname><given-names>Andrea</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref><xref ref-type="aff" rid="aff5"><sup>5</sup></xref></contrib><contrib contrib-type="author"><name><surname>Riva</surname><given-names>Giuseppe</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref><xref ref-type="aff" rid="aff5"><sup>5</sup></xref></contrib></contrib-group><aff id="aff1"><label>1</label><addr-line>Informatics and Telematics Institute, Centre for Research and Technology Hellas, Thermi, Thessaloniki, Greece</addr-line></aff><aff id="aff2"><label>2</label><addr-line>Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece</addr-line></aff><aff id="aff3"><label>3</label><addr-line>Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom</addr-line></aff><aff id="aff4"><label>4</label><addr-line>Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy</addr-line></aff><aff id="aff5"><label>5</label><addr-line>Psychology Department, Catholic University of Milan, Milan, Italy</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Zalla</surname><given-names>Tiziana</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1"><addr-line>Ecole Normale Supérieure, France</addr-line></aff><author-notes><corresp id="cor1">* E-mail: <email>dgiakoum@iti.gr</email></corresp><fn fn-type="conflict"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type="con"><p>Conceived and designed the experiments: DG DT AD PC AG GR GH. Performed the experiments: DG. Analyzed the data: DG PC. Contributed reagents/materials/analysis tools: DG AD. Wrote the paper: DG DT AD PC AG GR GH.</p></fn></author-notes><pub-date pub-type="collection"><year>2012</year></pub-date><pub-date pub-type="epub"><day>19</day><month>9</month><year>2012</year></pub-date><volume>7</volume><issue>9</issue><elocation-id>e43571</elocation-id><history><date date-type="received"><day>9</day><month>12</month><year>2011</year></date><date date-type="accepted"><day>26</day><month>7</month><year>2012</year></date></history><permissions><copyright-year>2012</copyright-year><copyright-holder>Giakoumis et al</copyright-holder><license><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><abstract><p>This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing.</p></abstract><funding-group><funding-statement>This study has been made possible thanks to partial funding from European project, “INTERSTRESS – Interreality in the management and treatment of stress-related disorders” (FP7-247685). No additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement></funding-group><counts><page-count count="16"></page-count></counts></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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