Automated Pain Recognition Using Analysis of Facial Expressions

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atmire.migration.oldid6133
dc.contributor.advisorYanushkevich, Svetlana
dc.contributor.authorShier, Warren Adam
dc.contributor.committeememberNielsen, John
dc.contributor.committeememberShahbazi, Mozhdeh
dc.date.accessioned2017-10-03T18:52:21Z
dc.date.available2017-10-03T18:52:21Z
dc.date.issued2017
dc.date.submitted2017en
dc.description.abstractCurrent pain evaluation involves the use of patient self-reporting, which can be subjective, prone to suggestion, and infeasible on certain patients. Post-surgery patients, elderly people with dementia, or young children cannot properly convey their pain, even though it still occurs. There are also limitations on the frequency caregivers or doctors can check on their patients. To help solve this problem, this thesis develops solutions for automated pain detection via facial expressions. The system continually classifies the subject as being in pain, or not in pain. Subject pain levels are verified using the Prkachin and Solomon Pain Intensity Scale. Two fully automated algorithms are presented, the first uses Gabor filters with Support Vector Machines, the other uses a type of deep learning, Convolutional Neural Networks. Feasibility studies are conducted on a database and real-life subjects from an elderly care facility. Results are evaluated using precisions and speed of computation.en_US
dc.identifier.citationShier, W. A. (2017). Automated Pain Recognition Using Analysis of Facial Expressions (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25076en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/25076
dc.identifier.urihttp://hdl.handle.net/11023/4203
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectArtificial Intelligence
dc.subjectComputer Science
dc.subjectEngineering--Electronics and Electrical
dc.subjectPsychology--Physiological
dc.subject.otherConvolutional Neural Networks
dc.subject.otherAutomated Pain Recognition
dc.subject.otherFacial Expression Recognition
dc.subject.otherSupport Vector Machines
dc.subject.otherGabor Filters
dc.titleAutomated Pain Recognition Using Analysis of Facial Expressions
dc.typemaster thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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