Prediction of activity type in preschool children using machine learning techniques.

Journal: Journal of science and medicine in sport
Published Date: Jul 1, 2015

Abstract

OBJECTIVES: Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children.

Authors

  • Markus Hagenbuchner
    School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2500, Australia.
  • Dylan P Cliff
    Faculty of Social Sciences, Early Start Research Institute, University of Wollongong, Australia. Electronic address: dylanc@uow.edu.au.
  • Stewart G Trost
    Institute of Health and Biomedical Innovation, Queensland University of Technology, Australia. Electronic address: s.trost@qut.edu.au.
  • Nguyen Van Tuc
    Faculty of Engineering and Information Science, University of Wollongong, Australia. Electronic address: vtn966@uow.edu.au.
  • Gregory E Peoples
    School of Medicine, University of Wollongong, Australia. Electronic address: greg_peoples@uow.edu.au.

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