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 2103950919 1984146303 2531425913
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. The computational study of human metabolism has been advanced with the advent of the first generic (non-tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue-specific genome-scale models of human metabolism. The algorithm generates a tissue-specific model from the generic human model by integrating a variety of tissue-specific molecular data sources, including literature-based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome-scale stoichiometric model of hepatic metabolism. The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions. The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms. 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. The aim was to determine whether the midpoint between ventilatory thresholds (MPVT) corresponds to maximal lactate steady state (MLSS). Twelve amateur cyclists (21.0 ± 2.6 years old; 72.2 ± 9.0 kg; 179.8 ± 7.5 cm) performed an incremental test (25 W·min-1) until exhaustion and several constant load tests of 30 minutes to determine MLSS, on different occasions. Using MLSS determination as the reference method, the agreement with five other parameters (MPVT; first and second ventilatory thresholds: VT1 and VT2; respiratory exchange ratio equal to 1: RER = 1.00; and Maximum) was analysed by the Bland-Altman method. The difference between workload at MLSS and VT1, VT2, RER=1.00 and Maximum was 31.1 ± 20.0, -86.0 ± 18.3, -63.6 ± 26.3 and -192.3 ± 48.6 W, respectively. MLSS was underestimated from VT1 and overestimated from VT2, RER = 1.00 and Maximum. The smallest difference (-27.5 ± 15.1 W) between workload at MLSS and MPVT was in better agreement than other analysed parameters of intensity in cycling. The main finding is that MPVT approached the workload at MLSS in amateur cyclists, and can be used to estimate maximal steady state.