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

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dc.contributor.authorDelgado-Gonzalo, Ricard
dc.contributor.authorRenevey, Philippe
dc.contributor.authorCalvo, Enric M.
dc.contributor.authorSolà, Josep
dc.contributor.authorLanting, Cees
dc.contributor.authorBertschi, Mattia
dc.contributor.authorLemay, Mathieu
dc.date.accessioned2022-02-14T17:07:44Z
dc.date.available2022-02-14T17:07:44Z
dc.date.issued2014
dc.description.abstractIn 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 biopotentials 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.
dc.identifier.citationDigital Human Modeling. Applications in Health, Safety, Ergonomics and Risk Management, Heraklion (GR), pp. 3-14
dc.identifier.doi10.1007/978-3-319-07725-3_1
dc.identifier.isbn978-3-319-07724-6 978-3-319-07725-3
dc.identifier.urihttps://hdl.handle.net/20.500.12839/609
dc.identifier.urlhttp://link.springer.com/10.1007/978-3-319-07725-3_1
dc.titleHuman Energy Expenditure Models: Beyond State-of-the-Art Commercialized Embedded Algorithms
dc.typeProceedings Article
dc.type.csemdivisionsBU-D
dc.type.csemresearchareasDigital Health
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