VO(2)max Prediction From Submaximal Exercise Test Using Artificial Neural Network

Akay M. F. , Akturk E., Baliki A.

21st Signal Processing and Communications Applications Conference (SIU), CYPRUS, 24 - 26 April 2013 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Country: CYPRUS


The goal of this study is to develop an accurate artificial neural network (ANN)-based model to predict maximal oxygen uptake (VO(2)max) of fit adults from a single stage submaximal treadmill jogging test. Participants (81 males and 45 females), aged from 17 to 40 years, successfully completed a maximal graded exercise test (GXT) to determine VO(2)max. The variables; gender, age, body mass, steady-state heart rate and jogging speed are used to build the ANN prediction model. Using 10-fold cross validation on the dataset, the average values of standard error of estimate (SEE) and multiple correlation coefficient (R) of the model are calculated as 1.80 ml.kg(-1).ml(-1) and 0.93, respectively. Compared with the results of the other prediction models in literature that were developed using Multiple Linear Regression Analysis, the reported values of SEE and R in this study are consider-ably more accurate.