Using absolute intensity methods (metabolic equivalent of energy (METs), etc.) to determine exercise intensity in exercise prescriptions is straightforward and convenient. Using relative intensity methods (heart rate reserve (%HRR), maximal heart rate (%HR_max), etc.) is more recommended because it is more personalized. Taking target heart rate (THR) given by the relative method as an example, compared with just presenting the THR value, intuitively providing the setting parameters for achieving the THR with specific sport equipment is more user-friendly. The objective of this study was to find a method which combines the advantages (convenient and personalized) of the absolute and relative methods and relatively avoids their disadvantages, helping individuals to meet the target intensity by simply setting equipment parameters. For this purpose, we recruited 32 males and 29 females to undergo incremental cardiopulmonary exercise testing with cycling equipment. The linear regression model of heart rate and exercise wattage (the setting parameter of the equipment) was constructed for each one (R² = 0.933, p < 0.001), and the slopes of the graph of these models were obtained. Next, we used an iterative algorithm to obtain a multiple regression model (adjusted R² = 0.8336, p < 0.001) of selected static body data and the slopes of participants. The regression model can accurately predict the slope of the general population through their static body data. Moreover, other populations can guarantee comparable accuracy by using questionnaire data for calibration. Then, the predicted slope can be utilized to calculate the equipment’s settings for achieving a personalized THR through our equation. All of these steps can be assigned to the intelligent system.

Rating of perceived exertion (RPE) can serve as a more convenient and economical alternative to heart rate (HR) for exercise intensity control. This study aims to explore the influence of factors, such as indicators of demographic, anthropometric, body composition, cardiovascular function and basic exercise ability on the relationship between HR and RPE, and to develop the model predicting RPE from HR. 48 healthy participants were recruited to perform an incrementally 6-stage pedaling test. HR and RPE were collected during each stage. The influencing factors were identified with the forward selection method to train Gaussian Process regression (GPR), support vector machine (SVM) and linear regression models. Metrics of R2, adjusted R2 and RMSE were calculated to evaluate the performance of the models. The GPR model outperformed the SVM and linear regression models, and achieved an R2 of 0.95, adjusted R2 of 0.89 and RMSE of 0.52. Indicators of age, resting heart rate (RHR), Central arterial pressure (CAP), body fat rate (BFR) and body mass index (BMI) were identified as factors that best predicted the relationship between RPE and HR. It is possible to use GPR model to estimate RPE from HR accurately, after adjusting for age, RHR, CAP, BFR and BMI.

Maximum data in exercise (Max-Ex), including maximum heart rate (HR_max), peak oxygen uptake (VO_2pk), maximum power (MaxP), etc., are frequently used, whether it is for the determination of exercise intensity, the measurement of an athlete’s performance, assessment of recovery from disease, and so on. However, very often this choice does not take into account the targeted individual. We recruited 32 males and 29 females to undergo an incremental graded exercise test (GXT). Therefore, our study seeks to determine variations in Max-Ex, according to the noninvasive static human data (Non-In data). Data showed a significant relationship (p < 0.001) between body composition and Max-Ex. Of the 41 types of Non-In data we collected in communities, the body composition generally showed high correlation (maximum r = 0.839). 57.5% of the data, of which r > 0.6 were about body composition. The muscle-related body composition data had a greater effect on power, and the fat-related ones had a greater effect on HR_max and VO_2pk. For some types of Max-Ex, the older and younger ones showed specific differences. Therefore, these results can be employed to adequately prescribe personalized health promotion programs according to diversity and availability, and have some reference value for other studies using Max-Ex.