Multi-modal pain intensity recognition based on the SenseEmotion database

Thiam, Patrick; Kessler, Viktor; Amirian, Mohammadreza; Bellmann, Peter; Layher, Georg; Zhang, Yan; Velana, Maria; Gruss, Sascha; Walter, Steffen; Traue, Harald C.; Schork, Daniel; Kim, Jonghwa; Andre, Elisabeth; Neumann, Heiko; Schwenker, Friedhelm

doi:10.1109/TAFFC.2019.2892090

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Multi-modal pain intensity recognition based on the SenseEmotion database
Authors:	Thiam, Patrick Kessler, Viktor Amirian, Mohammadreza Bellmann, Peter Layher, Georg Zhang, Yan Velana, Maria Gruss, Sascha Walter, Steffen Traue, Harald C. Schork, Daniel Kim, Jonghwa Andre, Elisabeth Neumann, Heiko Schwenker, Friedhelm
et. al:	No
DOI:	10.1109/TAFFC.2019.2892090
Published in:	IEEE Transactions on Affective Computing
Volume(Issue):	12
Issue:	3
Page(s):	743
Pages to:	760
Issue Date:	2021
Publisher / Ed. Institution:	IEEE
ISSN:	1949-3045
Language:	English
Subjects:	Pain; Database; Physiology; Computer architecture; Electromyography; Feature extraction; Reliability
Subject (DDC):	616: Internal medicine and diseases
Abstract:	The 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.
URI:	https://nbn-resolving.org/urn:nbn:de:bvb:384-opus4-898887 https://digitalcollection.zhaw.ch/handle/11475/27097
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Computer Science (InIT)
Appears in collections:	Publikationen School of Engineering

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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.