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Aerobic Fitness Level Estimation Using Wearables
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SYSNO ASEP 0571440 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Aerobic Fitness Level Estimation Using Wearables Author(s) 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)Number of authors 14 Article number 302 Source Title 2022 Computing in Cardiology (CinC). - New York : IEEE, 2022 - ISSN 2325-8861 - ISBN 979-8-3503-0097-0 Pages roč. 49 (2022) Number of pages 4 s. Publication form Online - E Action Computing in Cardiology 2022 /49./ Event date 04.09.2022 - 07.09.2022 VEvent location Tampere Country FI - Finland Event type WRD Language eng - English Country US - United States Keywords Aerobic Fitness Level ; Cardiorespiratory fitness Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Institutional support UPT-D - RVO:68081731 EID SCOPUS 85152959956 DOI 10.22489/CinC.2022.302 Annotation 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. Workplace Institute of Scientific Instruments Contact Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Year of Publishing 2024 Electronic address https://ieeexplore.ieee.org/document/10081645
Number of the records: 1