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]

Aktarla ,EceAkay, Mehmet FatihAktürk, ErmanAçikkar, Mustafa12.07.20192019-07-1212.07.20192019-07-1220139781467355629https://doi.org/10.1109/SIU.2013.6531513https://hdl.handle.net/20.500.12507/4292013 21st Signal Processing and Communications Applications Conference, SIU 2013 -- 24 April 2013 through 26 April 2013 -- HaspolatArtificial 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.trinfo:eu-repo/semantics/openAccessArtificial Neural NetworksMaximal Oxygen UptakePredictionNeural 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]Conference Object2-s2.0-8488089591510.1109/SIU.2013.6531513N/A