Abstract
The Lactate threshold (LT) has gained special attention in the sport world and is considered one of the potential indicators to evaluate individual performance in different sports. Traditionally, measuring LT requires frequent collection of blood samples from individuals under specific spatiotemporal conditions. This procedure causes discomfort to individuals besides test related cost. In this paper, we propose a non-invasive model to estimate LT using a machine learning (ML) algorithm as a step towards eliminating the need of blood sample collection and facilitating non-invasive performance test. We train and test this model on a 100-subject dataset, which we constructed in collaboration with Peak Center for Human Performance. We also propose a method to fill the missing values in this dataset, which contains the collected data of real life incremental running tests performed at this Centre. We also shed the light on the correlation between demographic data and the LT occurrence and hence help determine the factors affecting LT estimation as a vital sign in the sport world. Applying a multi-layer perceptron (MLP) algorithm on the constructed dataset provided the best correlation coefficient of 0.7983 compared with the LT ground truth scores. Using different combinations of demographic data in conjunction with heart rate (HR) and speed in the training and testing provided various correlation coefficients, which are also presented in this paper.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Myers, J., Ashley, E.: Dangerous curves: a perspective on exercise, lactate, and the anaerobic threshold. Chest 111(3), 787–795 (1997)
Badawi, H.F., Dong, H., El Saddik, A.: Mobile cloud-based physical activity advisory system using biofeedback sensors. Future Gener. Comput. Syst. 66, 59–70 (2017)
Lamaarti, F., Arafsha, F., Hafidh, B., El Saddik, A.: Automated athlete haptic training system for soccer sprinting. In: Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019, pp. 303–309 (2019)
Faude, O., Kindermann, W., Meyer, T.: Lactate threshold concepts: how valid are they? Sports Med. 39(6), 469–490 (2009)
Hall, M.M., Rajasekaran, S., Thomsen, T.W., Peterson, A.R.: Lactate: friend or foe. PM R 8(3), S8–S15 (2016)
Nascimento, E.M.F., Kiss, M.A.P.D.M., Santos, T.M., Lambert, M., Pires, F.O.: Determination of lactate thresholds in maximal running test by heart rate variability data set. Asian J. Sports Med. 8(3), e58480 (2017)
Marques-Neto, S.R., Santos, E.L., Maior, A.S., Neto, G.A.M.: Analysis of heart rate deflection points to predict the anaerobic threshold by a computerized method. J. Strength Cond. Res. 26(7), 1967–1974 (2012)
Simões, R.P., Mendes, R.G., Castello-Simões, V., Catai, A.M., Arena, R., Borghi-Silva, A.: Use of heart rate variability to estimate lactate threshold in coronary artery disease patients during resistance exercise. J. Sports Sci. Med. 15(4), 649–657 (2016)
Garcia-Tabar, I., Llodio, I., Sánchez-Medina, L., Ruesta, M., Ibañez, J., Gorostiaga, E.M.: Heart rate-based prediction of fixed blood lactate thresholds in professional team-sport players. J. Strength Cond. Res. 29(10), 2794–2801 (2015)
Nikooie, R., Gharakhanlo, R., Rajabi, H., Bahraminegad, M., Ghafari, A.: Noninvasive determination of anaerobic threshold by monitoring the %SpO2 changes and respiratory gas exchange. J. Strength Cond. Res. 23(7), 2107–2113 (2009)
Wasserman, K., Whipp, B.J., Koyal, S.N., Beaver, W.L.: Anaerobic threshold and respiratory gas exchange during exercise. J. Appl. Physiol. 35(2), 236–243 (1973)
Llodio, I., Garcia-Tabar, I., Sánchez-Medina, L., Ibáñez, J., Gorostiaga, E.M.: Estimation of the maximal lactate steady state in junior soccer players. Int. J. Sports Med. 36(14), 1142–1148 (2015)
Mason, A., et al.: Noninvasive in-situ measurement of blood lactate using microwave sensors. IEEE Trans. Biomed. Eng. 65(3), 698–705 (2018)
Arteaga-Falconi, J.S., et al.: Dtwins: a digital twins ecosystem for health and well-being. IEEE COMSOC MMTC Commun. Front. 14(2), 39–43 (2019)
Henriet, J.: Artificial intelligence-virtual trainer: an educative system based on artificial intelligence and designed to produce varied and consistent training lessons. Proc. Inst. Mech. Eng. Part P: J. Sports Eng. Technol. 231(2), 110–124 (2017)
Fister Jr., I., Ljubič, K., Suganthan, P.N., Perc, M., Fister, I.: Computational intelligence in sports: challenges and opportunities within a new research domain. Appl. Math. Comput. 262, 178–186 (2015)
Erdogan, A., Cetin, C., Goksu, H., Guner, R., Baydar, M.L.: Non-invasive detection of the anaerobic threshold by a neural network model of the heart rate-work rate relationship. Proc. Inst. Mech. Eng. Part P: J. Sports Eng. Technol. 223(3), 109–115 (2009)
Peak Centre For Human Performance. https://www.peakcentre.ca/. Accessed 20 Aug 2019
Fitness Assessment. https://www.peakcentre.ca/individual-training/personal-fitness-assessment/. Accessed 20 Aug 2019
Multilayer Perceptrons. http://users.ics.aalto.fi/ahonkela/dippa/node41.html. Accessed 12 Sep 2019
Nicholson, C.: A beginner’s guide to multilayer perceptrons (MLP). https://skymind.ai/wiki/multilayer-perceptron#code. Accessed 12 Sept 2019
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Badawi, H.F., Laamarti, F., Brunet, K., McNeely, E., El Saddik, A. (2020). Non-invasive Lactate Threshold Estimation Using Machine Learning. In: McDaniel, T., Berretti, S., Curcio, I., Basu, A. (eds) Smart Multimedia. ICSM 2019. Lecture Notes in Computer Science(), vol 12015. Springer, Cham. https://doi.org/10.1007/978-3-030-54407-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-54407-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-54406-5
Online ISBN: 978-3-030-54407-2
eBook Packages: Computer ScienceComputer Science (R0)