Individual cardiorespiratory fitness exercise prescription in elderly based on BP neural network.

Cardiorespiratory fitness (CRF) declines as age increases in elderly. An individualized CRF exercise prescription can maintain the CRF level and delay aging process. Traditional exercise prescriptions are general and lack of individualization. In this paper, a new study based on back-propagation (BP) neural network, is investigated to predict the individualized CRF exercise prescriptions for elderly by correlate variables (age, sex, BMI, VO2max initial value, improvement etc.). The raw data are split to two parts, 90% for training the machine and the remaining 10% for testing the performance. Based on a database with 2078 people, the exercise prescription prediction models MAE, RMSE and R2 are1.5206,1.4383 and 0.9944. 26 female subjects aged 60-79 years are recruited to test the models validity. The VO2maxs expected improvement was set at 10%. Based on the basic information of these elder women, we get personalized exercise prescription (frequency, intensity, time and volume) of each subject. All of them finished their own exercise intervention. The results show that the post VO2max was significantly different from the pre VO2max and improved by 10.1%, and a total of 20 subjects(74.1%) improved within one standard deviation and 25 subjects(92.6%)improved within 1.96 times standard deviations. Our study shows that a high degree of accuracy in exercise suggestions for elderly was achieved by applying the BP neural network model.