Abstract

Heart rate can be considered as a reliable indicator of the physiological load both for immediate training monitoring and for post-training analysis in cycling. The aim of this paper is to present a dynamic heart rate prediction model which will be used by a model predictive controller to optimize the cycling training. This model predicts the heart rate of a cyclist online during training or competition based on the physical dynamics of the heart rate to exercise work load. It uses eight parameters to calculate the future heart rate from current values, exercise duration and exercise work load by taking consideration of other effects such as fatigue, exhaustion and recovery. These parameters are identified from training data of nine well-trained cyclists by the least squares method. Each cyclist performed first a stepwise incremental test on a bicycle ergometer to determine his individual anaerobic threshold. Afterwards, they executed two interval tests on the same bicycle ergometer according to their individual anaerobic thresholds for the identification and evaluation of the model parameters. For all subjects, the mean absolute error and standard deviation between the measured and modelled heart rate values without updating are 3.06 and 3.95 bpm respectively. The mean correlation coefficient is 0.9686. If the model output is updated with the measured values every 20 seconds, then the mean absolute error is 1.31 bpm, the standard deviation is 1.92 bpm and the mean correlation coefficient is 0.9907. The result indicates that this model is able to predict the heart rate of cyclists accurately and can be used by a model predictive controller for training optimization. Verf.-Referat