Human Energy Expenditure Models: Beyond State-of-the-Art Commercialized Embedded Algorithms

  • Ricard Delgado-Gonzalo
  • Philippe Renevey
  • Enric M. Calvo
  • Josep Solà
  • Cees Lanting
  • Mattia Bertschi
  • Mathieu Lemay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8529)

Abstract

In the present study, we propose three new energy expenditure (EE) methods and evaluate their accuracy against state-of-the-art EE estimation commercialized devices. To this end, we used several sensors on 8 subjects to simultaneously record acceleration forces from wrist-located sensors and bio-potentials estimated from chest-located ECG devices. These subjects followed a protocol that included a wide range of intensities in a given set of activities, ranging from sedentary to vigorous. The results of the proposed human EE models were compared to indirect calorimetry EE estimated values (kcal/kg/h). The speed-based, heart rate-based and hybrid-based models are characterized by an RMSE of 1.22 ± 0.34 kcal/min, 1.53 ± 0.48 kcal/min and 1.03 ± 0.35 kcal/min, respectively. Based on the presented results, the proposed models provide a significant improvement over the state-of-the-art.

Keywords

energy expenditure walking/running speed human model physical activity monitoring 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ricard Delgado-Gonzalo
    • 1
  • Philippe Renevey
    • 1
  • Enric M. Calvo
    • 1
  • Josep Solà
    • 1
  • Cees Lanting
    • 2
  • Mattia Bertschi
    • 1
  • Mathieu Lemay
    • 1
  1. 1.CSEM, Signal ProcessingNeuchâtelSwitzerland
  2. 2.CSEM, Marketing & Business DevelopmentNeuchâtelSwitzerland

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