Application of Artificial Intelligence in Infant Movement Classification: A Reliability and Validity Study in Infants Who Were Full-Term and Preterm
Journal
Physical therapy
Journal Volume
104
Journal Issue
2
Date Issued
2024-02-01
Author(s)
Lin, Shiang-Chin
Chandra, Erick
Hsu, Jane Yung-Jen
Abstract
Preterm infants are at high risk of neuromotor disorders. Recent advances in digital technology and machine learning algorithms have enabled the tracking and recognition of anatomical key points of the human body. It remains unclear whether the proposed pose estimation model and the skeleton-based action recognition model for adult movement classification are applicable and accurate for infant motor assessment. Therefore, this study aimed to develop and validate an artificial intelligence (AI) model framework for movement recognition in full-term and preterm infants.
Subjects
Artificial Intelligence; Assessment; Machine Learning; Motor Development
SDGs
Type
journal article