Artificial intelligence algorithms effectively classify 38 movements in infants born full-term and preterm recorded in the laboratory and at home.

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Title: Artificial intelligence algorithms effectively classify 38 movements in infants born full-term and preterm recorded in the laboratory and at home.
Authors: Purwanto Y; School and Graduate Institute of Physical Therapy, National Taiwan University, Taipei, Taiwan., Chandra E; Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan., Tsao PN; Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan., Yen TA; Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan., Liao WC; Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan., Chen WJ; National Health Research Institute, Zhunan, Miaoli County, Taiwan., Liao CY; Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan., Hsieh CW; School and Graduate Institute of Physical Therapy, National Taiwan University, Taipei, Taiwan., Hsu JY; Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan; College of Intelligent Computing, Chang-Gung University, Taipei, Taiwan. Electronic address: yjhsu@csie.ntu.edu.tw., Jeng SF; School and Graduate Institute of Physical Therapy, National Taiwan University, Taipei, Taiwan; Physical Therapy Centre, National Taiwan University Hospital, Taipei, Taiwan. Electronic address: jeng@ntu.edu.tw.
Source: Journal of the Formosan Medical Association = Taiwan yi zhi [J Formos Med Assoc] 2026 Jul; Vol. 125 (7), pp. 789-798. Date of Electronic Publication: 2025 Aug 14.
Publication Type: Journal Article
Journal Info: Publisher: Formosan Medical Association, Elsevier Country of Publication: Singapore NLM ID: 9214933 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 0929-6646 (Print) Linking ISSN: 09296646 NLM ISO Abbreviation: J Formos Med Assoc Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:0929-6646
DOI:10.1016/j.jfma.2025.08.008