Infant Care Video Dataset for Classification of Interventions Using Transformers

Abstract:Healthcare documentation in the neonatal intensive care unit (NICU) presents significant challenges, with nurses spending approximately 25\% of their time on record-keeping, while up to 60\% of interventions remain undocumented. Motivated by the need to detect interventions from video automatically, we present the Infant Care Video Dataset (ICVD), a collection of 4,144 videos spanning 12 simulated intervention classes designed for developing automated documentation systems. Our manikin-based approach systematically varies conditions, such as camera angle and clinician skin tone, while ensuring privacy compliance. Using video transformer architectures (TimeSformer and MotionFormer), we establish strong baseline performance (93.97\% and 93.17\% top-1 accuracy) among the 12 infant care classes. Our ablation study comparing temporal models with a framewise approach (23.17\% accuracy) demonstrates a 70.80\% performance gap, validating the need for temporal modeling. The ICVD provides a foundation for developing automated documentation systems to reduce clinical burden in neonatal care environments and improve existing practices.

Published in the 11th IEEE International Workshop on Medical Computing (MediComp 2025), part of IEEE COMPSAC 2025. Dataset: this https URL

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

I.2.10; I.4.8; J.3

arXiv:2608.23838 [cs.CV]

(or arXiv:2608.23838v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2608.23838 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 2130-2135, 2025

https://doi.org/10.1109/COMPSAC65507.2025.00299 Focus to learn more DOI(s) linking to related resources

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