Application of Artificial Intelligence in Infant Movement Classification: A Reliability and Validity Study in Infants Who Were Full-Term and Preterm.

Journal: Physical therapy
PMID: 38245806

Abstract

OBJECTIVE: 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.

Authors

  • Shiang-Chin Lin
    School and Graduate Institute of Physical Therapy, National Taiwan University College of Medicine, Taipei, Taiwan.
  • Erick Chandra
    Department of Computer Science, National Taiwan University College of Electric Engineering and Computer Science, Taipei, Taiwan.
  • Po Nien Tsao
    Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan.
  • Wei-Chih Liao
    Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan; Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan. Electronic address: david.ntuh@gmail.com.
  • Wei-J Chen
    Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
  • Ting-An Yen
    Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan.
  • Jane Yung-Jen Hsu
    Department of Computer Science, National Taiwan University College of Electric Engineering and Computer Science, Taipei, Taiwan.
  • Suh-Fang Jeng
    School and Graduate Institute of Physical Therapy, National Taiwan University College of Medicine, Taipei, Taiwan.

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