Neural Network Based VO(2)max Prediction Models Using Maximal Exercise and Non-Exercise Data
| dc.authorid | Akay, Mehmet Fatih -- 0000-0003-0780-0679; Acikkar, Mustafa -- 0000-0001-8888-4987 | |
| dc.contributor.author | Aktarla, Ece | |
| dc.contributor.author | Akay, M. F. | |
| dc.contributor.author | Akturk, Erman | |
| dc.contributor.author | Acikkar, Mustafa | |
| dc.date.accessioned | 12.07.201910:50:10 | |
| dc.date.accessioned | 2019-07-12T15:28:03Z | |
| dc.date.available | 12.07.201910:50:10 | |
| dc.date.available | 2019-07-12T15:28:03Z | |
| dc.date.issued | 2013 | |
| dc.department | Fen Edebiyat Fakültesi | en_US |
| dc.description | 21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS | en_US |
| dc.description | WOS: 000325005300353 | en_US |
| dc.description.abstract | Artificial 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.identifier.isbn | 978-1-4673-5563-6; 978-1-4673-5562-9 | |
| dc.identifier.issn | 2165-0608 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12507/604 | |
| dc.language.iso | tr | |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartof | 2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | |
| dc.relation.ispartofseries | Signal Processing and Communications Applications Conference | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Artificial neural networks | en_US |
| dc.subject | maximal oxygen uptake | en_US |
| dc.subject | prediction | en_US |
| dc.title | Neural Network Based VO(2)max Prediction Models Using Maximal Exercise and Non-Exercise Data | |
| dc.type | Conference Object |
