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dc.contributor.authorWerner, Philipp
dc.contributor.authorLopez-Martinez, Daniel
dc.contributor.authorWalter, Steffen
dc.contributor.authorAl-Hamadi, Ayoub
dc.contributor.authorGruss, Sascha
dc.contributor.authorPicard, Rosalind W.
dc.date.accessioned2021-10-27T20:35:40Z
dc.date.available2021-10-27T20:35:40Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/1721.1/136497
dc.description.abstractIEEE Automated tools for pain assessment have great promise but have not yet become widely used in clinical practice. In this survey paper, we review the literature that proposes and evaluates automatic pain recognition approaches, and discuss challenges and promising directions for advancing this field. Prior to that, we give an overview on pain mechanisms and responses, discuss common clinically used pain assessment tools, and address shared datasets and the challenge of validation in the context of pain recognition.
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.isversionof10.1109/TAFFC.2019.2946774
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourceOther repository
dc.titleAutomatic Recognition Methods Supporting Pain Assessment: A Survey
dc.typeArticle
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratory
dc.relation.journalIEEE Transactions on Affective Computing
dc.eprint.versionAuthor's final manuscript
dc.type.urihttp://purl.org/eprint/type/JournalArticle
eprint.statushttp://purl.org/eprint/status/PeerReviewed
dc.date.updated2021-07-06T13:46:58Z
dspace.orderedauthorsWerner, P; Lopez-Martinez, D; Walter, S; Al-Hamadi, A; Gruss, S; Picard, R
dspace.date.submission2021-07-06T13:46:59Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Needed


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