Please use this identifier to cite or link to this item:
http://cmuir.cmu.ac.th/jspui/handle/6653943832/77592
Title: | Pain Detection Using Deep Learning Method from 3D Facial Expression and Movement of Motion |
Authors: | Kornprom Pikulkaew Varin Chouvatut |
Authors: | Kornprom Pikulkaew Varin Chouvatut |
Keywords: | Computer Science;Engineering |
Issue Date: | 1-Jan-2023 |
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. |
URI: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85135091457&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/77592 |
ISSN: | 23673389 23673370 |
Appears in Collections: | CMUL: Journal Articles |
Files in This Item:
There are no files associated with this item.
Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.