Predicting Children’s Energy Expenditure during Physical Activity using Deep Learning and Wearable Sensor Data

Abdul Hamid, Michael Duncan, Emma Eyre, Yanguo Jing

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
158 Downloads (Pure)


This study examined a series of machine learning models, evaluating their effectiveness in assessing children’s energy expenditure, in terms of the metabolic equivalents (MET) of physical activity (PA), from triaxial accelerometery. The study also determined the impact of the sensor placement (waist, ankle or wrist) on the machine learning model’s predictive performance. Twenty eight healthy Caucasian children aged 8-11years (13 girls, 15 boys) undertook a series of activities reflective of different levels of PA (lying supine, seated and playing with Lego, slow walking, medium walking, and a medium paced run, instep passing a football, overarm throwing and catching and stationary cycling). Energy expenditure and physical activity were assessed during all activities using accelerometers (GENEActiv monitor) worn on four locations (i.e. non-dominant wrist, dominant wrist, dominant waist, dominant ankle) and breath-by-breath calorimetry data. MET values ranged from 1.2 ± 0.2 for seated playing with Lego to 4.1 ± 0.8 for running at 6.5kmph-1. Machine learning models were used to determine the MET values from the accelerometer data and to determine which placement location performed more effectively in predicting the PA data. The study identified that novel machine learning models can be used to accurately predict METs, with 90% accuracy. The models showed a preference towards the dominant wrist or ankle as the movement in those positions were more consistent during PA. It was evident that machine learning models using these locations can be effectively used to accurately predict METs for PA in children.
Original languageEnglish
Pages (from-to)918-926
Number of pages9
JournalEuropean Journal of Sport Science
Issue number6
Early online date16 Jul 2020
Publication statusPublished - 2021

Bibliographical note

This is an Accepted Manuscript of an article published by Taylor & Francis in 2020 on 16/07/2020, available online:

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.


  • GENEActiv
  • Indirect calorimetry
  • accelerometer
  • ankle
  • energy expenditure
  • machine learning
  • waist
  • wrist

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine
  • Physical Therapy, Sports Therapy and Rehabilitation


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