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Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry.

Claas LendtNiklas HansenIngo FroböseTom Stewart
Published in: The international journal of behavioral nutrition and physical activity (2024)
The integration of thigh-worn accelerometers with machine learning models provides a highly accurate method for classifying physical activity types and estimating energy expenditure. Our novel composite model approach improves the accuracy of energy expenditure measurements and supports better monitoring and assessment methods in non-laboratory settings.
Keyphrases
  • machine learning
  • physical activity
  • body mass index
  • soft tissue
  • high resolution
  • depressive symptoms
  • sleep quality
  • clinical evaluation