Application of Back-propagation Neural Network to Formulate Exercise Prescription for Taiwanese College Students

Abstract

In this study, we attempted to apply back-propagation neural network (BPNN) to formulate exercise prescriptions for Taiwanese college students. The purpose was to realize a rapid and accurate estimation of the exercise prescription for students. Three thousand college students of both sexes aged 19–24 participated in this study. Data on five physical fitness test parameters were collected, including subjects’ age, body mass index (BMI), and performance in three exercises: sit and reach, 1-minute bent-leg curl-ups, and running. The data were then randomly divided into two groups: training samples (n = 1800) and testing samples (n = 1200). Next, BPNN was utilized to estimate the exercise prescription level of the samples. The sample data was divided to examine the learning ability of BPNN. The BPNN network structure for this study encompasses an input layer (5 units), a hidden layer (5 units), and an output layer (4 units). The learning rate of the BPNN was assumed to be 0.5, and its learning cycle consisted of 850 rounds. The results indicated that the mean accuracy rate for estimating the prescription level was 93.22% for training samples and 92.38% for testing samples. In other words, the mean relative error was 6.78% for training samples and 7.62% for testing samples, both of which were within the acceptable range. These results indicate that applying BPNN to formulate an exercise prescription is feasible. Furthermore, because it is rapid and accurate, BPNN could prove to be a better option than manual assessment. Further, computer applications based on the BPNN technology can be developed to assist teachers and coaches in formulating student exercise prescriptions, thus conserving the cost and labor that would otherwise be required in the case of manual assessment