Free-living Evaluation of Laboratory-based Activity Classifiers in Preschoolers.

Journal: Medicine and science in sports and exercise
PMID: 31764460

Abstract

UNLABELLED: Machine learning classification models for accelerometer data are potentially more accurate methods to measure physical activity in young children than traditional cut point methods. However, existing algorithms have been trained on laboratory-based activity trials, and their performance has not been investigated under free-living conditions.

Authors

  • Matthew N Ahmadi
    Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
  • Denise Brookes
    Institute of Health and Biomedical Innovation at Queensland Centre for Children's Health Research, Queensland University of Technology, South Brisbane, AUSTRALIA.
  • Alok Chowdhury
    Faculty of Science and Engineering, School of Computer Science and Electrical Engineering, Queensland University of Technology, Brisbane, AUSTRALIA.
  • Toby Pavey
    Faculty of Health, School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, AUSTRALIA.
  • Stewart G Trost
    Institute of Health and Biomedical Innovation, Queensland University of Technology, Australia. Electronic address: s.trost@qut.edu.au.

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