Title: Estimation of lactate threshold with machine learning techniques in recreational runners,
Abstract: 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 centre. 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.