Determination of Body Fat Percentage by Gender Based with Photoplethysmography Signal Using Machine Learning Algorithm

Basit Öğe Kaydı
dc.contributor.authorAkman, M.
dc.contributor.authorUcar, M. K.
dc.contributor.authorUcar, Z.
dc.contributor.authorUcar, K.
dc.contributor.authorBarakli, B.
dc.contributor.authorBozkurt, M. R.
dc.date.accessioned2024-03-13T10:34:58Z
dc.date.available2024-03-13T10:34:58Z
dc.date.issued2022
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractObjective: Calculation of body fat percentage (BFP) is a frequently encountered problem in the literature. BFP is one of the most significant parameters which should be processed in body weight control programs. Anthropometric measurements and statistical methods are being used generally in the literature for BFP estimation. Artificial intelligence and gender-based models with a photoplethysmography signal (PPG) were proposed for BFP estimation in this study. Material and Methods: In the study, the PPG signal is divided into lower frequency bands, and 25 features are taken out from each frequency band. Artificial intelligence algorithms were created by reducing the extracted features with the help of a feature selection algorithm. Results: According to the results obtained, models with performance values of RMSE = 0.35, R =1 for men, RMSE = 0.87, R =1 for women were created. Conclusions: In the best performing models, the PPG signal's high-frequency components are used for men, whereas the low-frequency band of the PPG signal is used for women. As a result, the proposed model in this study is considered to be used for BFP measurement.en_US
dc.identifier.doi10.1016/j.irbm.2020.12.003
dc.identifier.endpage186en_US
dc.identifier.issn1959-0318
dc.identifier.issn1876-0988
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85099309602en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage169en_US
dc.identifier.urihttps://doi.org/10.1016/j.irbm.2020.12.003
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4179
dc.identifier.volume43en_US
dc.identifier.wosWOS:000809732400004en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Incen_US
dc.relation.ispartofIrbmen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPhotoplethysmography signalen_US
dc.subjectMachine learningen_US
dc.subjectBody compositionen_US
dc.subjectBody fat percentageen_US
dc.subjectGender-based body fat percentageen_US
dc.titleDetermination of Body Fat Percentage by Gender Based with Photoplethysmography Signal Using Machine Learning Algorithmen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu