Abstract:

Maximal oxygen consumption (maxVO2) is a direct indicator of aerobic capacity. For this reason, maxVO2 measurement is of great importance both in sport branches and also in clinic. However, the fact that maxVO2 measurement systems are costly has led to the need to determine different analysis methods. In this study, it was aimed to predict maxVO2 values with machine learning models using anthropometric, kinematic, heart rate and step parameters. MaxVO2 values and heart rates of 52 male athletes participating in the study at three different running speeds on the treadmill were determined and evaluated together with anthropometric and kinematic data. Age, height, body weight, heart rate, leg length, thigh length, running speed, stride frequency, stride length parameters were presented as input to the machine learning models and the calculation of the maxVO2 value was made. In addition, four different machine learning models (Linear Regression, Support Vector Machines, Decision Trees, and Gaussian Process Regression) were used and the most successful approach was examined. The Gaussian Process Regression model was able to determine the maxVO2 value with the most successful prediction (R2=0.99) and the lowest error rate (RMSE=0.012). As a result, maxVO2 values were successfully estimated in both submaximal and maximal values using basic anthropometric measurements (height, body weight, leg and thigh length), heart rate, speed and stride parameters (stride frequency and stride length) within the scope of the study.

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