Paper Title
Maximum Oxygen Uptake Prediction for Normal and Inclined Walking from Exercise Factors with Physical Characteristics using Artificial Neural Networks

Abstract
The current research aimed to predict maximum oxygen uptake (VO2max) of fit adults fornormal and inclined (10° and 20°)walking using artificial neural network (ANN).60subjectsaccomplished a submaximal treadmillwalking test (TWT) to determine VO2max. A multi–layered perceptron (MLP) feed–forward ANN trained by the Broyden–Fletcher–Goldfarb–Shanno (BGFS) algorithm with k–fold cross–validation was utilized to predict the VO2max. The age, gender, body mass index, heart rate (HR) at rest, andHRof different inclinationswere set as input parameters/signals of the ANN.The optimum arrangement of the ANN structure for VO2max predictionprovided the root mean square error (RMSE) and the R–squared(R2)in range of 0.0257 to 0.1083ml kg–1 min–1 and 0.9953 to 0.9999, respectively.The results of the current research were compared with other developed prediction models in the literature andfound substantiallyimproved. Keywords - Gradient of Walking; Inclined Surfaces; Oxygen Uptake; Modelinghuman Performance.