Artificial Neural Network-Based Model for Evaluating Maximum Oxygen Uptake from the Incremental Squatting Test in Young People

Abstract

Background: Currently, there are two methods for testing maximal oxygen uptake: the direct and indirect methods, but both have certain requirements for testing equipment, site, and personnel. There is a lack of a convenient and effective method for testing maximum oxygen uptake (VO2max). With the development of artificial neural network (ANN), a solution to this gap is provided. Objective: The goal of this study was to design a method to evaluate the cardiopulmonary function of young people and verify its feasibility and reliability. Methods: The incremental squat test (IST) and Young Men’s Christian Association (YMCA) test were designed with 196 subjects (97 males and 99 females). The back propagation (BP) neural network was used to construct the model of VO2max by recording and analyzing squatting times, height, weight, gender, age, leg length, Manou Riers Skelic index (MRSI), and VO2max. Results: Three hidden layers and 65 nodes were employed in the BP neural network. Each hidden layer contained 19 nodes. Other parameters of this network were 0.01, 0.9, and 2000 for the learning rate, momentum, and iterations, respectively. The difference between the measurements and predictions was not significant (p > 0.05), and the correlation between them was extremely strong (r = 0.98, p < 0.01). Conclusions: We conclude that the model constructed using the BP neural network is accurate, and the IST is feasible for predicting VO2max. This method can be used as a substitute for other cardiopulmonary fitness test protocols in cases of insufficient venues and equipment. thereby preventing health complications. In subsequent studies, the sample size should be expanded, and separate prediction models should be developed for different genders.