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dc.contributor.authorAktarla, Ece
dc.contributor.authorAkay, M. F.
dc.contributor.authorAkturk, Erman
dc.contributor.authorAcikkar, Mustafa
dc.date.accessioned12.07.201910:50:10
dc.date.accessioned2019-07-12T15:28:03Z
dc.date.available12.07.201910:50:10
dc.date.available2019-07-12T15:28:03Z
dc.date.issued2013
dc.identifier.isbn978-1-4673-5563-6; 978-1-4673-5562-9
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/20.500.12507/604
dc.description21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUSen_US
dc.descriptionWOS: 000325005300353en_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(2)max) the input variables of the dataset are gender, age, body mass index (BMI), grade, self-reported 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 VO(2)max 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.language.isoturen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesSignal Processing and Communications Applications Conference
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectmaximal oxygen uptakeen_US
dc.subjectpredictionen_US
dc.titleNeural Network Based VO(2)max Prediction Models Using Maximal Exercise and Non-Exercise Dataen_US
dc.typeconferenceObjecten_US
dc.relation.journal2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)en_US
dc.contributor.departmentFen Edebiyat Fakültesien_US
dc.contributor.authorIDAkay, Mehmet Fatih -- 0000-0003-0780-0679; Acikkar, Mustafa -- 0000-0001-8888-4987en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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