TY - GEN
T1 - Deep learning model for detection of pain intensity from facial expression
AU - Soar, Jeffrey
AU - Bargshady, Ghazal
AU - Zhou, Xujuan
AU - Whittaker, Frank
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Many people who are suffering from a chronic pain face periods of acute pain and resulting problems during their illness and adequate reporting of symptoms is necessary for treatment. Some patients have difficulties in adequately alerting caregivers to their pain or describing the intensity which can impact on effective treatment. Pain and its intensity can be noticeable in ones face. Movements in facial muscles can depict ones current emotional state. Machine learning algorithms can detect pain intensity from facial expressions. The algorithm can extract and classify facial expression of pain among patients. In this paper, we propose a new deep learning model for detection of pain intensity from facial expressions. This automatic pain detection system may help clinicians to detect pain and its intensity in patients and by doing this healthcare organizations may have access to more complete and more regular information of patients regarding their pain.
AB - Many people who are suffering from a chronic pain face periods of acute pain and resulting problems during their illness and adequate reporting of symptoms is necessary for treatment. Some patients have difficulties in adequately alerting caregivers to their pain or describing the intensity which can impact on effective treatment. Pain and its intensity can be noticeable in ones face. Movements in facial muscles can depict ones current emotional state. Machine learning algorithms can detect pain intensity from facial expressions. The algorithm can extract and classify facial expression of pain among patients. In this paper, we propose a new deep learning model for detection of pain intensity from facial expressions. This automatic pain detection system may help clinicians to detect pain and its intensity in patients and by doing this healthcare organizations may have access to more complete and more regular information of patients regarding their pain.
UR - http://www.scopus.com/inward/record.url?scp=85049995973&partnerID=8YFLogxK
UR - https://www.icost-society.org/
U2 - 10.1007/978-3-319-94523-1_22
DO - 10.1007/978-3-319-94523-1_22
M3 - Conference contribution
AN - SCOPUS:85049995973
SN - 9783319945224
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 249
EP - 254
BT - Smart Homes and Health Telematics, Designing a Better Future
A2 - Mokhtari, Mounir
A2 - Abdulrazak, Bessam
A2 - Aloulou, Hamdi
PB - Springer
CY - Switzerland
T2 - 16th International Conference on Smart homes, Assistive Technologies and Health Telematics, ICOST 2018
Y2 - 10 July 2018 through 12 July 2018
ER -