Gated Recirculation Unit (GRU) modelling for the prediction of oxygen consumption during cycling

Monitoring oxygen consumption during cycling is crucial for performance and health management. Due to the time series characteristics of oxygen consumption data, it is particularly important to select a suitable model for prediction. However, despite the advantages of Gated Recurrent Unit (GRU) model in processing time series data, its application in the field of cycling oxygen consumption prediction is still limited. Therefore, this study used GRU model to predict oxygen consumption during riding, and compared it with Multi-Layer Perceptron (MLP), AutoRegressive Integrated Moving Average (ARIMA), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) models. The results show that the GRU model has the best performance in forecasting accuracy and error control, and its evaluation indexes are: the Mean Absolute Error (MAE) is 0.0440, the Mean Square Error (MSE) is 0.0035, the coefficient of determination (R²) is 0.9273, and the prediction accuracy is 90.59%.