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.
In this regard, a ML application has also been used to improve the estimation of the HRDP and thus solving one of the weak points of the methodology originally proposed by Conconi @cite_0 . More precisely, proposed to use non-linear autoregresive exogenous (NARX) models in combination with fuzzy interpolation to model the heart rate (HR) dynamics and to find the HRDP. However, the accuracy obtained was low and the population studied very small (9 athletes) and therefore its not reasonable to draw conclusions about its applicability, specially about the generalization capabilities of the model.
@cite_0
1984146303
The interface between skelet al muscle activation through aerobic and anaerobic glycolysis is of key interest to sportspeople and athletes who participate in medium to long distance sports, such as middle- and long-distance running, cycling, swimming, rowing, kayaking and a variety of other events. To date, the gold standard for measuring anaerobic threshold (AT) is a structured test to exhaustion where blood lactate concentration is measured at regular intervals. However, the need for invasive testing, requiring trained personnel and specialist equipment, limits the availability of such tests. This paper proposes a non-invasive AT measurement method, which validates well against AT measured using lactate analysis. In addition, the proposed test has a relatively loose set of requirements on the exercise test protocol required and just requires a measure of exercise intensity and heart-rate. While the test is applicable to a range of sports, usage is demonstrated in this paper for a set of cyclists, using velocity as a measure of exercise intensity.