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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/VJ02010031" target="_blank" >VJ02010031: Modulární multisenzorický profesní oděv k řízení rizika, ochraně zdraví a bezpečnosti členů IZS pomocí metod umělé inteligence</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

AI

ISSN

2673-2688

e-ISSN

2673-2688

Svazek periodika

5

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

12

Strana od-do

2914-2925

Kód UT WoS článku

001384119100001

EID výsledku v databázi Scopus

2-s2.0-85213420918