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Predicting Energy Expenditure in Preschool Children Using Accelerometer and Gyroscope Data.

Hannah J Coyle-Asbil1,2,3, Katarina Osojnicki1, Alexa Robertson1

  • 1Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, ON,Canada.

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|February 11, 2026
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Summary
This summary is machine-generated.

Adding gyroscope data to accelerometer data slightly improved energy expenditure prediction in preschoolers, but the gains were minimal. Accelerometer-only models are recommended for practical use due to simpler data collection.

Keywords:
calibrationmachine learningphysical activitysedentary behaviorwearables

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Area of Science:

  • Biomedical Engineering
  • Pediatric Exercise Science
  • Wearable Technology

Background:

  • Accurate estimation of energy expenditure (EE) in preschool children is crucial for understanding their physical activity levels and metabolic health.
  • Wearable sensors offer a promising, non-invasive method for monitoring EE in free-living conditions.
  • Previous research has explored accelerometer data for EE prediction, but the added value of gyroscope data remains less clear.

Purpose of the Study:

  • To investigate whether integrating gyroscopic data with accelerometer data enhances the prediction accuracy of energy expenditure in preschool children.
  • To compare the performance of different sensor configurations (accelerometer-only, gyroscope-only, and dual-sensor) for EE prediction.
  • To evaluate various machine learning models for predicting EE using different sensor inputs and wear locations.

Main Methods:

  • Thirty-nine preschool children (aged 3 to <6 years) participated in the study.
  • Participants wore multiple devices (OPAL, GT9X, GENEActiv) on different body locations while energy expenditure was measured using a portable metabolic unit.
  • Fifty-four machine learning models were developed and evaluated using root mean squared error, comparing accelerometer, gyroscope, and dual-sensor data with different algorithms and wear sites.

Main Results:

  • The random forest model incorporating both accelerometer and gyroscope data (dual-sensor) demonstrated marginally lower root mean squared error compared to other configurations in most instances.
  • While dual-sensor models showed slight improvements, the enhancement in energy expenditure prediction accuracy was minimal across various settings.
  • No significant differences were observed in model performance based on the specific EE measure (metabolic equivalents or kilojoules per minute) or wear location.

Conclusions:

  • The addition of gyroscope data to accelerometer data provides only marginal improvements in predicting energy expenditure in preschool children.
  • Considering the minimal gains and potential challenges in data acquisition and processing for dual-sensor systems, accelerometer-based models are recommended for future research.
  • Future studies should focus on optimizing accelerometer-based algorithms for robust and practical energy expenditure estimation in young children.