Automatic Recognition Methods Supporting Pain Assessment: A Survey
Name
Werner19_PainRecognitionSurvey_PublicDownload-1.pdf
Description
Accepted version
Size
1.25 MB
Format
Adobe PDF
Checksum (MD5)
6f9c05db90814e586677e921c779b2ee
Author(s)
Werner, Philipp
Lopez-Martinez, Daniel
Walter, Steffen
Al-Hamadi, Ayoub
Gruss, Sascha
Picard, Rosalind W.
Date Issued
2019
Journal
IEEE Transactions on Affective Computing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
IEEE Automated tools for pain assessment have great promise but have not yet become widely used in clinical practice. In this survey paper, we review the literature that proposes and evaluates automatic pain recognition approaches, and discuss challenges and promising directions for advancing this field. Prior to that, we give an overview on pain mechanisms and responses, discuss common clinically used pain assessment tools, and address shared datasets and the challenge of validation in the context of pain recognition.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/TAFFC.2019.2946774
https://doi.org/10.1109/TAFFC.2019.2946774
