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Computer Science > Machine Learning

arXiv:2212.04764 (cs)
[Submitted on 9 Dec 2022]

Title:AuE-IPA: An AU Engagement Based Infant Pain Assessment Method

Authors:Mingze Sun, Haoxiang Wang, Wei Yao, Jiawang Liu
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Abstract:Recent studies have found that pain in infancy has a significant impact on infant development, including psychological problems, possible brain injury, and pain sensitivity in adulthood. However, due to the lack of specialists and the fact that infants are unable to express verbally their experience of pain, it is difficult to assess infant pain. Most existing infant pain assessment systems directly apply adult methods to infants ignoring the differences between infant expressions and adult expressions. Meanwhile, as the study of facial action coding system continues to advance, the use of action units (AUs) opens up new possibilities for expression recognition and pain assessment. In this paper, a novel AuE-IPA method is proposed for assessing infant pain by leveraging different engagement levels of AUs. First, different engagement levels of AUs in infant pain are revealed, by analyzing the class activation map of an end-to-end pain assessment model. The intensities of top-engaged AUs are then used in a regression model for achieving automatic infant pain assessment. The model proposed is trained and experimented on YouTube Immunization dataset, YouTube Blood Test dataset, and iCOPEVid dataset. The experimental results show that our AuE-IPA method is more applicable to infants and possesses stronger generalization ability than end-to-end assessment model and the classic PSPI metric.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2212.04764 [cs.LG]
  (or arXiv:2212.04764v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2212.04764

Submission history

From: Mingze Sun [view email]
[v1] Fri, 9 Dec 2022 10:41:22 UTC (4,796 KB)
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