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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Tarih
2013
Dergi Başlığı
Dergi ISSN
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Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Artificial 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.
Açıklama
2013 21st Signal Processing and Communications Applications Conference, SIU 2013 -- 24 April 2013 through 26 April 2013 -- Haspolat
Anahtar Kelimeler
Artificial Neural Networks, Maximal Oxygen Uptake, Prediction
Kaynak
2013 21st Signal Processing and Communications Applications Conference, SIU 2013
WoS Q Değeri
Scopus Q Değeri
N/A
