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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kornprom Pikulkaew | en_US |
dc.contributor.author | Varin Chouvatut | en_US |
dc.date.accessioned | 2022-10-16T07:48:47Z | - |
dc.date.available | 2022-10-16T07:48:47Z | - |
dc.date.issued | 2023-01-01 | en_US |
dc.identifier.issn | 23673389 | en_US |
dc.identifier.issn | 23673370 | en_US |
dc.identifier.other | 2-s2.0-85135091457 | en_US |
dc.identifier.other | 10.1007/978-981-19-2394-4_67 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85135091457&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/77592 | - |
dc.description.abstract | Nowadays, face expression technology is widespread. For instance, 2D pain detection is utilized in hospitals; nevertheless, it has some disadvantages that should be considered. Our goal was to design a 3D pain detection system that anybody may use before coming to the hospital, supporting all orientations. We utilized a dataset from the University of Northern British Columbia (UNBC) as a training set in this study. Pain is classified as not hurting, becoming painful, and painful in our system. The system’s effectiveness was established by comparing its results to those of a highly trained medical and two-dimensional pain identification. To conclude, our study has developed an uncomplicated, cost-effective, and easy to comprehend alternative tool for screening for pain before admission for the public in general and health provider. | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Engineering | en_US |
dc.title | Pain Detection Using Deep Learning Method from 3D Facial Expression and Movement of Motion | en_US |
dc.type | Book Series | en_US |
article.title.sourcetitle | Lecture Notes in Networks and Systems | en_US |
article.volume | 464 | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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