Achieve personalized exercise intensity through an intelligent system and cycling equipment: A machine learning approach

Abstract

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 (%HRmax), 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.