Computer Vision to the Rescue: Infant Postural Symmetry Estimation from Incongruent Annotations

Huang, Xiaofei; Wan, Michael; Luan, Lingfei; Bethany, Tunik,; Ostadabbas, Sarah

doi:10.1109/wacv56688.2023.00195

Cited by 8 publications

(1 citation statement)

References 16 publications

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“…Persistent asymmetrical body behaviour in early life provides a prominent prodromal risk marker of neurodevelopmental conditions like autism spectrum disorder and congenital muscular torticollis. The authors in [135] proposed a computer vision method for assessing bilateral infant postural symmetry from images, based on 3D human pose estimation, adapted to the challenging setting of infant bodies. In particular, the HW-HuP interactive annotation tool was modified to correct 3D poses predicted on infants in the SyRIP dataset.…”

Section: Work (Yearmentioning

confidence: 99%

Computer Vision Tasks for Ambient Intelligence in Children’s Health

Germanese,

Colantonio,

Del Coco

et al. 2023

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Computer vision is a powerful tool for healthcare applications since it can provide objective diagnosis and assessment of pathologies, not depending on clinicians’ skills and experiences. It can also help speed-up population screening, reducing health care costs and improving the quality of service. Several works summarise applications and systems in medical imaging, whereas less work is devoted to surveying approaches for healthcare goals using ambient intelligence, i.e., observing individuals in natural settings. Even more, there is a lack of papers providing a survey of works exhaustively covering computer vision applications for children’s health, which is a particularly challenging research area considering that most existing computer vision technologies have been trained and tested only on adults. The aim of this paper is then to survey, for the first time in the literature, the papers covering children’s health-related issues by ambient intelligence methods and systems relying on computer vision.

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Section: Work (Yearmentioning

confidence: 99%