Accuracy Improvement of Energy Expenditure Estimation Through Neural Networks: A Pilot Study

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F24%3A00379646" target="_blank" >RIV/68407700:21460/24:00379646 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.3390/ai5040140" target="_blank" >https://doi.org/10.3390/ai5040140</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/ai5040140" target="_blank" >10.3390/ai5040140</a>

Result language
angličtina
Original language name
Accuracy Improvement of Energy Expenditure Estimation Through Neural Networks: A Pilot Study
Original language description
The estimation of energy expenditure (EE) is often an integral part of algorithms for wearable electronics. In field practice, procedures based on an indirect estimation of EE from the heart rate (using empirically or statistically based relationships) work correctly only in a narrow range of physical loads, yet they are current considered state of the art. This pilot study aims to experimentally assess novel method using a wide range of input sensors and parameters (heart rate, RR intervals, and 3D motion activity in several places on the body) and neural network (NN) algorithms. Our proposed method consists of training an NN on a specific subject, with a specific set and placement of sensors during the so-called training run, using the golden standard method of indirect calorimetry as a reference. Then, the subject’s EE can be estimated using trained NN. The results of the experiments (carried out on a total of 12 subjects during various physical activities) show a statistically significant improvement in EE estimation with the new prospective method, and it outperforms the state-of-the-art method based on the heart rate and regression model.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering

Project
<a href="/en/project/VJ02010031" target="_blank" >VJ02010031: Modular multisensory professional clothing for risk management, health protection and safety of IRS members using artificial intelligence methods</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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