Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Technologies + Design for Social Integration Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Pain Detection Using Deep Learning Method from 3D Facial Expression and Movement of Motion

Authors : Kornprom Pikulkaew, Varin Chouvatut

Published in: Proceedings of Seventh International Congress on Information and Communication Technology

Publisher: Springer Nature Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Measuring E-government System Users’ Satisfaction Using a Multicriteria Analysis Model: A Case Study of Botswana
next chapter The Next-Generation 6G: Trends, Applications, Technologies, Challenges, and Use Cases
Literature
1.
Lucey P, Cohn JF et al (2012) Painful data: the UNBC-McMaster shoulder pain expression archive database. Image Vis Comput J 30:197–205CrossRef
2.
Hammal Z, Cohn JF (2012) Automatic detection of pain intensity. In: Multimodal interaction
3.
Vijayanandh R, Balakrishnan G (2010) Human face detection using color spaces and region property measures. In: IEEE international conference on robotics and vision
4.
Bargshady G, Zhou X, Wang H (2020) Enhanced deep learning algorithm development to detect pain intensity from facial expression images. Expert Syst Appl 149:1–10CrossRef
5.
Haque MA, Moeslund TB (2018) Deep multimodal pain recognition: a database and comparison of Spatio-Temporal visual modalities. In: Automatic face & gesture recognition
6.
Hicks CL, Von Baeyer CL et al (2001) The faces pain scale-revised: toward a common metric in pediatric pain measurement. Pain 93:173–183CrossRef
7.
Prkachin KM, Solomon PE (2008) The structure, reliability and validity of pain expression: evidence from patients with shoulder pain. Pain 139:267–274CrossRef
8.
He K, Zhang X et al (2020) Deep residual learning for image recognition. In: Computer vision and pattern recognition
9.
10.
Nityananda V, Read JC (2017) Stereopsis in animals: evolution, function and mechanisms. Experiment Biol 220:2502–2512CrossRef
11.
Pikulkaew K, Boonchieng E et al (2020) Pain detection using deep learning with evaluation system. In: Congress on information and communication technology
12.
Pikulkaew K, Chouvatut V (2021) Enhanced pain detection and movement of motion with data augmentation based on deep learning. In: Conference on knowledge and smart technology
13.
Pikulkaew K, Boonchieng W et al (2021) 2D facial expression and movement of motion for pain identification with deep learning methods. IEEE Access 9:109903–109914CrossRef
Metadata
Title
Pain Detection Using Deep Learning Method from 3D Facial Expression and Movement of Motion
Authors
Kornprom Pikulkaew
Varin Chouvatut
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Proceedings of Seventh International Congress on Information and Communication Technology

Print ISBN: 978-981-19-2393-7

Electronic ISBN: 978-981-19-2394-4

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-2394-4_67