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  2. School of Engineering
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  4. Publikationen School of Engineering
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dc.contributor.authorThiam, Patrick-
dc.contributor.authorKessler, Viktor-
dc.contributor.authorAmirian, Mohammadreza-
dc.contributor.authorBellmann, Peter-
dc.contributor.authorLayher, Georg-
dc.contributor.authorZhang, Yan-
dc.contributor.authorVelana, Maria-
dc.contributor.authorGruss, Sascha-
dc.contributor.authorWalter, Steffen-
dc.contributor.authorTraue, Harald C.-
dc.contributor.authorSchork, Daniel-
dc.contributor.authorKim, Jonghwa-
dc.contributor.authorAndre, Elisabeth-
dc.contributor.authorNeumann, Heiko-
dc.contributor.authorSchwenker, Friedhelm-
dc.date.accessioned2023-02-17T14:13:41Z-
dc.date.available2023-02-17T14:13:41Z-
dc.date.issued2021-
dc.identifier.issn1949-3045de_CH
dc.identifier.urihttps://nbn-resolving.org/urn:nbn:de:bvb:384-opus4-898887de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/27097-
dc.description.abstractThe subjective nature of pain makes it a very challenging phenomenon to assess. Most of the current pain assessment approaches rely on an individual’s ability to recognise and report an observed pain episode. However, pain perception and expression are affected by numerous factors ranging from personality traits to physical and psychological health state. Hence, several approaches have been proposed for the automatic recognition of pain intensity, based on measurable physiological and audiovisual parameters. In the current paper, an assessment of several fusion architectures for the development of a multi-modal pain intensity classification system is performed. The contribution of the presented work is two-fold: (1) 3 distinctive modalities consisting of audio, video and physiological channels are assessed and combined for the classification of several levels of pain elicitation. (2) An extensive assessment of several fusion strategies is carried out in order to design a classification architecture that improves the performance of the pain recognition system. The assessment is based on the SenseEmotion Database and experimental validation demonstrates the relevance of the multi-modal classification approach, which achieves classification rates of respectively 83.39% , 59.53% and 43.89% in a 2-class, 3-class and 4-class pain intensity classification task.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Transactions on Affective Computingde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectPainde_CH
dc.subjectDatabasede_CH
dc.subjectPhysiologyde_CH
dc.subjectComputer architecturede_CH
dc.subjectElectromyographyde_CH
dc.subjectFeature extractionde_CH
dc.subjectReliabilityde_CH
dc.subject.ddc616: Innere Medizin und Krankheitende_CH
dc.titleMulti-modal pain intensity recognition based on the SenseEmotion databasede_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1109/TAFFC.2019.2892090de_CH
zhaw.funding.euNode_CH
zhaw.issue3de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end760de_CH
zhaw.pages.start743de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume12de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Thiam, P., Kessler, V., Amirian, M., Bellmann, P., Layher, G., Zhang, Y., Velana, M., Gruss, S., Walter, S., Traue, H. C., Schork, D., Kim, J., Andre, E., Neumann, H., & Schwenker, F. (2021). Multi-modal pain intensity recognition based on the SenseEmotion database. IEEE Transactions on Affective Computing, 12(3), 743–760. https://doi.org/10.1109/TAFFC.2019.2892090
Thiam, P. et al. (2021) ‘Multi-modal pain intensity recognition based on the SenseEmotion database’, IEEE Transactions on Affective Computing, 12(3), pp. 743–760. Available at: https://doi.org/10.1109/TAFFC.2019.2892090.
P. Thiam et al., “Multi-modal pain intensity recognition based on the SenseEmotion database,” IEEE Transactions on Affective Computing, vol. 12, no. 3, pp. 743–760, 2021, doi: 10.1109/TAFFC.2019.2892090.
THIAM, Patrick, Viktor KESSLER, Mohammadreza AMIRIAN, Peter BELLMANN, Georg LAYHER, Yan ZHANG, Maria VELANA, Sascha GRUSS, Steffen WALTER, Harald C. TRAUE, Daniel SCHORK, Jonghwa KIM, Elisabeth ANDRE, Heiko NEUMANN und Friedhelm SCHWENKER, 2021. Multi-modal pain intensity recognition based on the SenseEmotion database. IEEE Transactions on Affective Computing [online]. 2021. Bd. 12, Nr. 3, S. 743–760. DOI 10.1109/TAFFC.2019.2892090. Verfügbar unter: https://nbn-resolving.org/urn:nbn:de:bvb:384-opus4-898887
Thiam, Patrick, Viktor Kessler, Mohammadreza Amirian, Peter Bellmann, Georg Layher, Yan Zhang, Maria Velana, et al. 2021. “Multi-Modal Pain Intensity Recognition Based on the SenseEmotion Database.” IEEE Transactions on Affective Computing 12 (3): 743–60. https://doi.org/10.1109/TAFFC.2019.2892090.
Thiam, Patrick, et al. “Multi-Modal Pain Intensity Recognition Based on the SenseEmotion Database.” IEEE Transactions on Affective Computing, vol. 12, no. 3, 2021, pp. 743–60, https://doi.org/10.1109/TAFFC.2019.2892090.


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