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
2052745868 2158265166 2008821584 1963753144
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. 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. We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability (HRV, an index for cardiac autonomic activity) were subsequently quantified and compared to corresponding measurements using Food and Drug Administration-approved sensors. High degrees of agreement were achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine. Abstract The purpose of this paper is twofold: (i) to investigate the emotion representation models and find out the possibility of a model with minimum number of continuous dimensions and (ii) to recognize and predict emotion from the measured physiological signals using multiresolution approach. The multimodal physiological signals are: Electroencephalogram (EEG) (32 channels) and peripheral (8 channels: Galvanic skin response (GSR), blood volume pressure, respiration pattern, skin temperature, electromyogram (EMG) and electrooculogram (EOG)) as given in the DEAP database. We have discussed the theories of emotion modeling based on i) basic emotions, ii) cognitive appraisal and physiological response approach and iii) the dimensional approach and proposed a three continuous dimensional representation model for emotions. The clustering experiment on the given valence, arousal and dominance values of various emotions has been done to validate the proposed model. A novel approach for multimodal fusion of information from a large number of channels to classify and predict emotions has also been proposed. Discrete Wavelet Transform, a classical transform for multiresolution analysis of signal has been used in this study. The experiments are performed to classify different emotions from four classifiers. The average accuracies are 81.45 , 74.37 , 57.74 and 75.94 for SVM, MLP, KNN and MMC classifiers respectively. The best accuracy is for ‘Depressing’ with 85.46 using SVM. The 32 EEG channels are considered as independent modes and features from each channel are considered with equal importance. May be some of the channel data are correlated but they may contain supplementary information. In comparison with the results given by others, the high accuracy of 85 with 13 emotions and 32 subjects from our proposed method clearly proves the potential of our multimodal fusion approach.