Automatic Detection of Pain from Facial Expressions: A Survey

Pain sensation is essential for survival, since it draws attention to physical threat to the body. Pain assessment is usually done through self-reports. However, self-assessment of pain is not available in the case of noncommunicative patients, and therefore, observer reports should be relied upon....

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2021-06, Vol.43 (6), p.1815-1831
Main Authors: Hassan, Teena, Seus, Dominik, Wollenberg, Johannes, Weitz, Katharina, Kunz, Miriam, Lautenbacher, Stefan, Garbas, Jens-Uwe, Schmid, Ute
Format: Article
Language:English
Subjects:
Automatic pain detection
Cognitive tasks
Computer vision
Encoding
facial expression analysis
facial expressions of pain
Feature extraction
Imaging
Machine learning
Observers
Pain
pain datasets
pain feature representation
survey
Task analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Pain sensation is essential for survival, since it draws attention to physical threat to the body. Pain assessment is usually done through self-reports. However, self-assessment of pain is not available in the case of noncommunicative patients, and therefore, observer reports should be relied upon. Observer reports of pain could be prone to errors due to subjective biases of observers. Moreover, continuous monitoring by humans is impractical. Therefore, automatic pain detection technology could be deployed to assist human caregivers and complement their service, thereby improving the quality of pain management, especially for noncommunicative patients. Facial expressions are a reliable indicator of pain, and are used in all observer-based pain assessment tools. Following the advancements in automatic facial expression analysis, computer vision researchers have tried to use this technology for developing approaches for automatically detecting pain from facial expressions. This paper surveys the literature published in this field over the past decade, categorizes it, and identifies future research directions. The survey covers the pain datasets used in the reviewed literature, the learning tasks targeted by the approaches, the features extracted from images and image sequences to represent pain-related information, and finally, the machine learning methods used.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2019.2958341