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.
Among these approaches, some studies tried to estimate the anaerobic threshold using other physiological parameters different to lactate. One of the most extended methods is based on the measurement of expired gases @cite_46 . This technique has shown to be an accurate solution when assessing the anaerobic threshold. Nevertheless, it still uses expensive and specialized equipment to make estimations and therefore, its not valid to fulfill our objectives. On the other hand, heart rate deflection point (HRDP) has also been used as indicator of the anaerobic threshold. The traditional study that put this relation into practice is known as Conconi Test @cite_34 . Since then, several other studies followed this approach while some others point out its deficiencies @cite_44 @cite_11 . These studies evidenced that, in spite of being a commonly used method, this method is far from being infallible. Factors such as lack of control of the conditions during and previous to the test and the impossibility of finding the HRDP in all the cases are probably among the reasons which make this method inaccurate.
@cite_44 @cite_46 @cite_34 @cite_11
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During an incremental run test, some researchers consistently observe a heart rate (HR) deflection at higher speeds, but others do not. The present study was designed to investigate whether differences in test protocols could explain the discrepancy. Additionally, we sought to determine whether the HR deflection point accurately predicts lactate threshold (LT). Eight trained runners performed four tests each: 1 ) a treadmill test for maximal O2 uptake, 2 ) a Conconi test on a 400-m track with speeds increasing ∼0.5 km h every 200 m, 3 ) a continuous treadmill run with speeds increasing 0.5 km h every minute, and 4 ) a continuous LT treadmill test in which 3-min stages were used. All subjects demonstrated an HR deflection on the track, but only one-half of the subjects showed an HR deflection on the treadmill. On the track the shortening of stages with increasing speeds contributed to a loss of linearity in the speed-HR relationship. Additionally, the HR deflection point overestimated the LT when a continuous treadmill LT protocol was used. In conclusion, the HR deflection point was not an accurate predictor of LT in the present study. 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. The relationship between running speed (RS) and heart rate (HR) was determined in 210 runners. On a 400-m track the athletes ran continuously from an initial velocity of 12–14 km h to submaximal velocities varying according to the athlete's capability. The HRs were determined through ECG. In all athletes examined, a deflection from the expected linearity of the RS-HR relationship was observed at submaximal RS. The test-retest correlation for the velocities at which this deflection from linearity occurred (Vd) determined in 26 athletes was 0.99. The velocity at the anaerobic threshold (AT), established by means of blood lactate measurements, and Vd were coincident in 10 runners. The correlation between Vd and average running speed (mean RS) in competition was 0.93 in the 5,000 m (mean Vd = 19.13 + - 1.08 km h; mean RS = 20.25 + - 1.15 km h), 0.95 in the marathon (mean Vd = 18.85 + - 1.15 km h; mean RS = 17.40 + - 1.14 km h), and 0.99 in the 1-h race (mean Vd = 18.70 + - 0.98 km h; mean RS = 18.65 + - 0.92 km h), thus showing that AT is critical in determining the running pace in aerobic competitive events. This paper examines the validity of the heart rate deflection point (HRDP) obtained with the ��updated�� Conconi test. Eleven male road cyclists performed 2 progressive incremental cycling tests and a 30-minute prolonged exercise test (PET). From the data obtained, comparisons were made and correlation coefficients were calculated between HRDP, the lactate threshold (LT), and the 3 mmol·L-1 threshold (AT3). The PET at HRDP demonstrated whether or not a steady state in blood lactate concentration (BLaSS) could be maintained. Significantly lower values for power output (p < 0.01) and heart rate (HR) (p < 0.01) were found at LT compared with HRDP. No differences were found between HRDP and AT3. Only a moderate correlation for power output between HRDP and AT3 (rs = 0.69; p < 0.05) could be observed. During the PET, only 6 out of 11 cyclists reached the target time of 30 minutes, and only 4 cyclists maintained a BLaSS. We conclude that the updated Conconi test is not a valid method for assessing LT or AT3. Therefore, this method seems not suitable to evaluate endurance performance and prescribe exercise intensities in road cycling.