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Aerobic Fitness Level Estimation Using Wearables
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SYSNO ASEP 0571440 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Aerobic Fitness Level Estimation Using Wearables Tvůrce(i) Smíšek, Radovan (UPT-D) RID, ORCID, SAI
Němcová, A. (CZ)
Smítal, L. (CZ)
Chlíbková, D. (CZ)
Králík, M. (CZ)
Kolářová, J. (CZ)
Myška, V. (CZ)
Kolařík, M. (CZ)
Harvánek, J. (CZ)
Arm, J. (CZ)
Baštán, O. (CZ)
Pospíšil, M. (CZ)
Šíma, J. (CZ)
Hubálek, J. (CZ)Celkový počet autorů 14 Číslo článku 302 Zdroj.dok. 2022 Computing in Cardiology (CinC). - New York : IEEE, 2022 - ISSN 2325-8861 - ISBN 979-8-3503-0097-0 Rozsah stran roč. 49 (2022) Poč.str. 4 s. Forma vydání Online - E Akce Computing in Cardiology 2022 /49./ Datum konání 04.09.2022 - 07.09.2022 Místo konání Tampere Země FI - Finsko Typ akce WRD Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova Aerobic Fitness Level ; Cardiorespiratory fitness Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Institucionální podpora UPT-D - RVO:68081731 EID SCOPUS 85152959956 DOI 10.22489/CinC.2022.302 Anotace Background: Aerobic fitness level (AFL) is a parameter closely related to a person's overall health. The gold standard of measurement is currently using expensive laboratory equipment. Aims: This study aimed to estimate AFL automatically using data measured with wearables. Methods: AFL was estimated in 2D space. The first dimension is the exertion level, and the second is the body's response to the exertion. Exertion level was determined based on metabolic equivalent calculated for each classified activity using the data of speed and elevation. The activity classification is based on deep neural networks. The body's response estimation is based on heart rate calculated from ECG or PPG. The test set contained 27 subjects. The reference was measured under laboratory conditions using the gold standard method. AFL classification by ACSM guidelines was used. Results: AFL determined by our algorithm were 0.44± 0.09,0.50± 0.10,0.53± 0.09, 0.58± 0.15, and 0.70± 0.07 for the reference classes very poor, poor, fair, good, and excellent, respectively. The correlation between the reference and determined values is 0.76. Conclusion: Our method showed promising results and will be further developed. Pracoviště Ústav přístrojové techniky Kontakt Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Rok sběru 2024 Elektronická adresa https://ieeexplore.ieee.org/document/10081645
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