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The babyPose dataset.

Lucia Migliorelli1, Sara Moccia1,2, Rocco Pietrini1

  • 1Department of Information Engineering, Università Politecnica delle Marche, Ancona (Italy).

Data in Brief
|October 21, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces the babyPose dataset, the first annotated depth image collection for analyzing preterm infant movement in neonatal intensive care units (NICUs). This resource aids AI development for clinical decision support.

Keywords:
Artificial intelligenceDepth imagesNeonatal intensive care unitsPose estimationPreterm infants

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Area of Science:

  • Medical Imaging
  • Neonatology
  • Computer Vision

Background:

  • Preterm infants in neonatal intensive care units (NICUs) require continuous monitoring.
  • Analyzing infant movement is crucial for developmental assessment and early intervention.
  • Existing datasets for infant movement analysis are limited, especially those using depth imaging.

Purpose of the Study:

  • To introduce and describe the babyPose dataset, a novel collection of annotated depth videos of preterm infants.
  • To provide a freely accessible resource for researchers developing AI algorithms for infant movement analysis in NICUs.
  • To facilitate the creation of decision support tools for healthcare professionals.

Main Methods:

  • Acquisition of 16 depth videos from preterm infants in a clinical NICU setting.
  • Each video contains 1000 frames, capturing 100 seconds of movement.
  • Annotation of 12 key limb-joint locations (shoulder, elbow, wrist, hip, knee, ankle) per frame.

Main Results:

  • The babyPose dataset is the first of its kind, offering annotated depth images of preterm infant movement.
  • The dataset includes detailed limb-joint annotations for advanced motion analysis.
  • It is publicly available for research purposes.

Conclusions:

  • The babyPose dataset is a valuable and unique resource for AI research in neonatal care.
  • It enables the development of advanced algorithms for objective assessment of preterm infant movement.
  • This dataset can significantly contribute to improving clinical decision-making in NICUs.