EMOPAIN Challenge 2020:Multimodal Pain Evaluation from Facial and Bodily Expressions

Egede, Joy O., Song, Siyang, Olugbade, Temitayo A., Wang, Chongyang, Williams, Amanda C.De C., Meng, Hongying, Aung, Min, Lane, Nicholas D., Valstar, Michel and Bianchi-Berthouze, Nadia (2020) EMOPAIN Challenge 2020:Multimodal Pain Evaluation from Facial and Bodily Expressions. In: Proceedings - 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020. Proceedings - 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020 . The Institute of Electrical and Electronics Engineers (IEEE), ARG, pp. 849-856. ISBN 9781728130798

Full text not available from this repository. (Request a copy)

Abstract

The EmoPain 2020 Challenge is the first international competition aimed at creating a uniform platform for the comparison of multi-modal machine learning and multimedia processing methods of chronic pain assessment from human expressive behaviour, and also the identification of pain-related behaviours. The objective of the challenge is to promote research in the development of assistive technologies that help improve the quality of life for people with chronic pain via real-time monitoring and feedback to help manage their condition and remain physically active. The challenge also aims to encourage the use of the relatively underutilised, albeit vital bodily expression signals for automatic pain and pain-related emotion recognition. This paper presents a description of the challenge, competition guidelines, bench-marking dataset, and the baseline systems' architecture and performance on the Challenge's three sub-tasks: pain estimation from facial expressions, pain recognition from multimodal movement, and protective movement behaviour detection.

Item Type:	Book Section
Additional Information:	Publisher Copyright: © 2020 IEEE.
Uncontrolled Keywords:	automatic pain assessment,facial expression analysis,pain related behaviour analysis,protective movement behaviour detection,computer vision and pattern recognition,artificial intelligence ,/dk/atira/pure/subjectarea/asjc/1700/1707
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Science > Research Groups > Health Computing Faculty of Science > Research Groups > Colour and Imaging Lab
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	13 Feb 2026 16:30
Last Modified:	13 Feb 2026 16:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101937
DOI:	10.1109/FG47880.2020.00078

Actions (login required)

View Item