Multimodal Assessment of Neonatal Pain Using Computer Vision
Infants receiving care in the Neonatal Intensive Care Unit (NICU) experience several
painful procedures during their hospitalization. Assessing neonatal pain is difficult
because the current standard for assessment is subjective, inconsistent, and discontinuous.
The intermittent and inconsistent assessment can induce poor treatment and, therefore,
cause serious longterm outcomes. The main aim of this project is to develop a robust
and comprehensive automatic system that generates a standardized pain assessment comparable
to those obtained by conventional nurse-derived pain scores. The continuous monitoring
of pain, using affordable, non-invasive, and easily integrable devices, provides immediate
pain detection and intervention, and therefore, contribute to improved long term outcomes;
i.e., reduce the outcomes of under- and over-treatment. It can also decrease caregivers’
bias and assessment burden. While further research is needed, the preliminary results
of our research showed that the automatic assessment of neonatal pain is a viable
and more efficient alternative to the manual assessment. More details »
Publications
M. S. Salekin, G. Zamzmi, D. Goldgof, R. Kasturi, T. Ho, Y. Sun (2020). "First Investigation
Into the Use of Deep Learning for Continuous Assessment of Neonatal Postoperative
Pain", IEEE International Conference on Automatic Face and Gesture Recognition.
M. S. Salekin, G. Zamzmi, R. Paul, D. Goldgof, R. Kasturi, T. Ho, Y. Sun (2019). "Harnessing
the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying
Sound", IEEE Healthcare Innovations and Point of Care Technologies Conference.
M. S. Salekin, G. Zamzmi, D. Goldgof, R. Kasturi, T. Ho, Y. Sun (2019). "Multi-Channel
Neural Network for Assessing Neonatal Pain from Videos", IEEE International Conference on Systems, Man, and Cybernetics.
G. Zamzmi, R. Paul, M. S. Salekin, D. Goldgof, R. Kasturi, T. Ho, Y. Sun (2019). "Convolutional
Neural Network for Neonatal Pain Assessment", IEEE Transactions on Biometrics, Behavior, and Identity Science.
G. Zamzmi, R. Paul, D. Goldgof, R. Kasturi, T. Ashmeade, Y. Sun (2019). "Pain Assessment
From Facial Expression: Neonatal Convolutional Neural Network (N-CNN)", International Joint Conference on Neural Networks.
G. Zamzmi, C.-Y. Pai, D. Goldgof, R. Kasturi, T. Ashmeade, Y. Sun (2019). "A Comprehensive
and Context-Sensitive Neonatal Pain Assessment Using Computer Vision", IEEE Transactions on Affective Computing.
G. Zamzmi, D. Goldgof, R. Kasturi, Y. Sun (2018). "Toward Ubiquitous Assessment of
Neonates' Health Condition", UbiComp.
R. Zhi, G. Zamzmi, D. Goldgof, T. Ashmeade, Y. Sun (2018). "Automatic Infants’ Pain
Assessment by Dynamic Facial Representation: Effects of Profile View, Gestational
Age, Gender, and Race", Journal of Clinical Medicine.
R. Zhi, G. Zamzmi, D. Goldgof, T. Ashmeade, T. Li, Y. Sun (2018). "Infants' Pain Recognition
Based on Facial Expression: Dynamic Hybrid Descriptions", IEICE Transactions on Information and Systems.
G. Zamzmi, R. Kasturi, D. Goldgof, R. Zhi, T. Ashmeade, Y. Sun (2018). "A Review of
Automated Pain Assessment in Infants: Features, Classification Tasks, and Databases",
IEEE Reviews in Biomedical Engineering.
G. Zamzmi, C.-Y. Pai, D. Goldgof, R. Kasturi, Y. Sun, T. Ashmeade (2017). "Automated
Pain Assessment in Neonates", Scandinavian Conference on Image Analysis.
G. Zamzmi, C.-Y. Pai, D. Goldgof, R. Kasturi, T. Ashmeade, Y. Sun (2016). "An Approach
for Automated Multimodal Analysis of Infants' Pain", International Conference on Pattern Recognition.
G. Zamzami, G. Ruiz, D. Goldgof, R. Kasturi, Y. Sun, T. Ashmeade (2015). "Pain Assessment
in Infants: Towards Spotting Pain Expression Based on Infants' Facial Strain", IEEE International Conference and Workshops on Automatic Face and Gesture Recognition.
