Neural Network Based VO(2)max Prediction Models Using Maximal Exercise and Non-Exercise Data
[ X ]
Tarih
2013
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS
WOS: 000325005300353
WOS: 000325005300353
Anahtar Kelimeler
Artificial neural networks, maximal oxygen uptake, prediction
Kaynak
2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)
