Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study.

Journal: JMIR mHealth and uHealth
PMID: 34346890

Abstract

BACKGROUND: Accurate solutions for the estimation of physical activity and energy expenditure at scale are needed for a range of medical and health research fields. Machine learning techniques show promise in research-grade accelerometers, and some evidence indicates that these techniques can be applied to more scalable commercial devices.

Authors

  • Ruairi O'Driscoll
    Appetite Control and Energy Balance Group, School of Psychology, University of Leeds , Leeds, UK.
  • Jake Turicchi
    Appetite Control and Energy Balance Group, School of Psychology, University of Leeds , Leeds, UK.
  • Mark Hopkins
    School of Food Science and Nutrition, Faculty of Mathematics and Physical Sciences, University of Leeds , Leeds, UK.
  • Cristiana Duarte
    Appetite Control and Energy Balance Group, School of Psychology, University of Leeds, Leeds, United Kingdom.
  • Graham W Horgan
    Biomathematics & Statistics Scotland , Aberdeen, UK.
  • Graham Finlayson
    Appetite Control and Energy Balance Group, School of Psychology, University of Leeds , Leeds, UK.
  • R James Stubbs
    Appetite Control and Energy Balance Group, School of Psychology, University of Leeds, Leeds, United Kingdom.

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