Individual lactate threshold estimation using recurrent neural networks.A standardization of the temporal axis to homogenize different length time-series.A modified stratified sampling for train-test set splitting of time-series.The system shows good individualization and generalization power.A non-invasive, cost efficient and accessible method to assess lactate threshold. Lactate threshold is considered an essential parameter when assessing performance of elite and recreational runners and prescribing training intensities in endurance sports. However, the measurement of blood lactate concentration requires expensive equipment and the extraction of blood samples, which are inconvenient for frequent monitoring. Furthermore, most recreational runners do not have access to routine assessment of their physical fitness by the aforementioned equipment so they are not able to calculate the lactate threshold without resorting to an expensive and specialized center. Therefore, the main objective of this study is to create an intelligent system capable of estimating the lactate threshold of recreational athletes participating in endurance running sports.The solution here proposed is based on a machine learning system which models the lactate evolution using recurrent neural networks and includes the proposal of standardization of the temporal axis as well as a modification of the stratified sampling method. The results show that the proposed system accurately estimates the lactate threshold of 89.52 of the athletes and its correlation with the experimentally measured lactate threshold is very high (R=0.89). Moreover, its behaviour with the test dataset is as good as with the training set, meaning that the generalization power of the model is high.Therefore, in this study a machine learning based system is proposed as alternative to the traditional invasive lactate threshold measurement tests for recreational runners.
Actually, @cite_19 proposed a mathematical model of human lactate metabolism gathering several physiological models and merging them into a single one. This model was then parametrized to fit to individual athletes. However, the authors acknowledged that this approach was not valid for all type of athletes, being more appropriate for high-speed sports. Moreover, the parametrization of the model required from multiple measurements including blood lactate measurements and a respiratory metabolism analysis among others. This leaves this study out of the scope of this work as it does not fulfill the objectives previously presented. In any case, this study highlights the complexity of the lactate modelling problem even if individualization is the only concern. The complexity increases if both accuracy and generalization are sought, as it is our case.
@cite_19
2161972280
Based on a mathematical model of the blood circulatory system, we construct a mathematical model for lactate metabolism in a human body. We pose the identification problem for lactate metabolism parameters by measurements. We develop the method, algorithm, and software for solving this identification problem. We also consider practical applications in sports medicine and the training process, in particular in our studies of the anaerobic threshold phenomenon and propose new methods for estimating the individual anaerobic threshold and maximal oxygen consumption for athletes.