Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/71895
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dc.contributor.authorKornprom Pikulkaewen_US
dc.contributor.authorEkkarat Boonchiengen_US
dc.contributor.authorWaraporn Boonchiengen_US
dc.contributor.authorVarin Chouvatuten_US
dc.date.accessioned2021-01-27T04:17:05Z-
dc.date.available2021-01-27T04:17:05Z-
dc.date.issued2021-01-01en_US
dc.identifier.issn21945365en_US
dc.identifier.issn21945357en_US
dc.identifier.other2-s2.0-85091919743en_US
dc.identifier.other10.1007/978-981-15-5859-7_42en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85091919743&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/71895-
dc.description.abstract© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Recent evidence has appeared that major enhancements in patient results can be increased by the periodical observing patient pain levels by medical staff in hospitals. Nevertheless, owing to the responsibility and pressure that the staffs have, this kind of observation has been complicated to withstand; thus, a system that works automatically could be the solution. Using an automatic facial expression system to detect pain which pain can be defined via several facial action units (AUs). To simplify pain detection using deep learning, data were collected from the UNBC database, which contains sequences of images that show participants’ faces while they were doing an arrangement of movement-of-motion tests. To improve pain detection using facial expressions, this research proposes a pain detection technique that uses deep learning. Finally, the resulting of the experiment will be compared with the self-reports, and doctors will be asked to evaluate the system.en_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.titlePain detection using deep learning with evaluation systemen_US
dc.typeBook Seriesen_US
article.title.sourcetitleAdvances in Intelligent Systems and Computingen_US
article.volume1184en_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsCenter of Excellence in Community Health Informaticsen_US
Appears in Collections:CMUL: Journal Articles

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