Neural network based VO2max prediction models using maximal exercise and non-exercise data [Maksimal egzersiz ve egzersize dayali olmayan verileri kullanarak sinir agi tabanli VO2MAX tahmin modelleri]

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dc.contributor.authorAktarla ,Ece
dc.contributor.authorAkay, Mehmet Fatih
dc.contributor.authorAktürk, Erman
dc.contributor.authorAçikkar, Mustafa
dc.date.accessioned12.07.201910:50:10
dc.date.accessioned2019-07-12T15:25:46Z
dc.date.available12.07.201910:50:10
dc.date.available2019-07-12T15:25:46Z
dc.date.issued2013
dc.departmentMeslek Yüksekokuluen_US
dc.description2013 21st Signal Processing and Communications Applications Conference, SIU 2013 -- 24 April 2013 through 26 April 2013 -- Haspolaten_US
dc.description.abstractArtificial Neural Network (ANN) models based on maximal and non-exercise (N-Ex) variables are developed to predict maximal oxygen uptake (VO 2max) the input variables of the dataset are gender, age, body mass index (BMI), grade, selfreported rating of perceived exertion (RPE) from treadmill test, heart rate (HR), perceived functional ability (PFA) and physical activity rating (PA-R). The performance of the models is evaluated by calculating their standard error of estimate (SEE) and multiple correlation coefficient (R). The results suggest that the performance of VO2max prediction models based on maximal and standard N-Ex variables (i.e. gender, age, BMI etc) can be improved by including questionnaire variables (PFA and PA-R) in the models.en_US
dc.identifier.doi10.1109/SIU.2013.6531513
dc.identifier.isbn9781467355629
dc.identifier.scopus2-s2.0-84880895915
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/SIU.2013.6531513
dc.identifier.urihttps://hdl.handle.net/20.500.12507/429
dc.indekslendigikaynakScopus
dc.language.isotr
dc.relation.ispartof2013 21st Signal Processing and Communications Applications Conference, SIU 2013
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectMaximal Oxygen Uptakeen_US
dc.subjectPredictionen_US
dc.titleNeural network based VO2max prediction models using maximal exercise and non-exercise data [Maksimal egzersiz ve egzersize dayali olmayan verileri kullanarak sinir agi tabanli VO2MAX tahmin modelleri]
dc.typeConference Object

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