Spatio-temporal assessment of pain intensity through facial transformation-based representation learning

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buir.advisorDibeklioğlu, Hamdi
dc.contributor.authorErekat, Diyala Nabeel Ata
dc.date.accessioned2021-09-23T08:23:56Z
dc.date.available2021-09-23T08:23:56Z
dc.date.copyright2021-09
dc.date.issued2021-09
dc.date.submitted2021-09-22
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Master's): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2021.en_US
dc.descriptionIncludes bibliographical references (leaves 65-76).en_US
dc.description.abstractThe nature of pain makes it di cult to assess due to its subjectivity and multidimensional characteristics that include intensity, duration, and location. However, the ability to assess pain in an objective and reliable manner is crucial for adequate pain management intervention as well as the diagnosis of the underlying medical cause. To this end, in this thesis, we propose a video-based approach for the automatic measurement of self-reported pain. The proposed method aims to learn an e cient facial representation by exploiting the transformation of one subject's facial expression to that of another subject's within a similar pain group. We also explore the e ect of leveraging self-reported pain scales i.e., the Visual Analog Scale (VAS), the Sensory Scale (SEN), and the A ective Motivational Scale (AFF), as well as the Observer Pain Intensity (OPI) on the reliable assessment of pain intensity. To this end, a convolutional autoencoder network is proposed to learn the facial transformation between subjects. The autoencoder's optimized weights are then used to initialize the spatio-temporal network architecture, which is further optimized by minimizing the mean absolute error of estimations in terms of each of these scales while maximizing the consistency between them. The reliability of the proposed method is evaluated on the benchmark database for pain measurement from videos, namely, the UNBC-McMaster Pain Archive. Despite the challenging nature of this problem, the obtained results show that the proposed method improves the state of the art, and the automated assessment of pain severity is feasible and applicable to be used as a supportive tool to provide a quantitative assessment of pain in clinical settings.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2021-09-23T08:23:56Z No. of bitstreams: 1 Spatio_Temporal_Assessment_of_Pain_Intensity_Through_Facial_Transformation_Based_Representation_Learning (signed).pdf: 6985407 bytes, checksum: f3a93b1ef0c138c9325ecfd545cd6f51 (MD5)en
dc.description.provenanceMade available in DSpace on 2021-09-23T08:23:56Z (GMT). No. of bitstreams: 1 Spatio_Temporal_Assessment_of_Pain_Intensity_Through_Facial_Transformation_Based_Representation_Learning (signed).pdf: 6985407 bytes, checksum: f3a93b1ef0c138c9325ecfd545cd6f51 (MD5) Previous issue date: 2021-09en
dc.description.statementofresponsibilityby Diyala Nabeel Ata Erekaten_US
dc.embargo.release2022-03-22
dc.format.extentxi, 76 leaves : illustrations, charts ; 30 cm.en_US
dc.identifier.itemidB133495
dc.identifier.urihttp://hdl.handle.net/11693/76542
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPainen_US
dc.subjectFacial expressionen_US
dc.subjectTemporalen_US
dc.subjectVisual analogue scaleen_US
dc.subjectAutoencoderen_US
dc.subjectConvolutional neural networken_US
dc.subjectRecurrent neural networken_US
dc.subjectDeep learningen_US
dc.titleSpatio-temporal assessment of pain intensity through facial transformation-based representation learningen_US
dc.title.alternativeYüz dönüşümü tabanlı gösterim öğrenimi ile ağrı şiddetinin uzam-zamansal değerlendirilmesien_US
dc.typeThesisen_US
thesis.degree.disciplineComputer Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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