Energy Expenditure Estimation in Children, Adolescents and Adults by Using a Respiratory Magnetometer Plethysmography System and a Deep Learning Model.

Journal: Nutrients
PMID: 36235842

Abstract

PURPOSE: Energy expenditure is a key parameter in quantifying physical activity. Traditional methods are limited because they are expensive and cumbersome. Additional portable and cheaper devices are developed to estimate energy expenditure to overcome this problem. It is essential to verify the accuracy of these devices. This study aims to validate the accuracy of energy expenditure estimation by a respiratory magnetometer plethysmography system in children, adolescents and adults using a deep learning model.

Authors

  • Fenfen Zhou
    Sino-French Joint Research Center of Sport Science, College of Physical Education and Health, East China Normal University, Shanghai 200241, China.
  • Xiaojian Yin
    Sino-French Joint Research Center of Sport Science, College of Physical Education and Health, East China Normal University, Shanghai 200241, China.
  • Rui Hu
    School of Automation and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Aya Houssein
    Département Sciences du Sport et Éducation Physique, Ecole Normale Supérieure de Rennes, 35170 Bruz, France.
  • Steven Gastinger
    Laboratoire Mouvement-Sport-Santé (EA 7404), Université de Rennes 2, 35170 Bruz, France.
  • Brice Martin
    Laboratoire Mouvement-Sport-Santé (EA 7404), Université de Rennes 2, 35170 Bruz, France.
  • Shanshan Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Jacques Prioux
    Département Sciences du Sport et Éducation Physique, Ecole Normale Supérieure de Rennes, 35170 Bruz, France.

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