Abstract
The positive influence of physical activity for people at all life stages is well known. Exercising has a proven therapeutic effect on the cardiovascular system and can counteract the increase of cardiovascular diseases in our aging society. An easy and good measure of the cardiovascular feedback is the heart rate. Being able to model and predict the response of a subject’s heart rate on work load input allows the development of more advanced smart devices and analytic tools. These tools can monitor and control the subject’s activity and thus avoid overstrain which would eliminate the positive effect on the cardiovascular system. Current heart rate models were developed for a specific scenario and evaluated on unique data sets only. Additionally, most of these models were tested in indoor environments, e.g. on treadmills and bicycle ergometers. However, many people prefer to do sports in outdoors environments and use their smart phone to record their training data. In this paper, we present an evaluation of existing heart rate models and compare their prediction performance for indoor as well as for outdoor running exercises. For this purpose, we investigate analytical models as well as machine learning approaches in two training sets: one indoor exercise set recorded on a treadmill and one outdoor exercise set recorded by a smart phone.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
WHO: Demographic change, life expectancy and mortality trends in europe: fact sheet. In: The European Health Report 2012. World Health Organization (2012)
Nichols, M., Townsend, N., Luengo-Fernandez, R., Leal, J., Gray, A., Scarborough, P., Rayner, M.: European Cardiovascular Disease Statistics 2012. European Heart Network, Brussels, European Society of Cardiology, Sophia Antipolis (2012)
Graf, C., Bjarnason-Wehrens, B., Rost, R., Foitschik, T., Lagerström, D., Quilling, E.: Sport-und Bewegungstherapie bei inneren Krankheiten: Lehrbuch für Sportlehrer, Übungsleiter, Physiotherapeuten und Sportmediziner. Deutscher Ärzte-Verlag (2014)
Leveille, S.G., Guralnik, J.M., Ferrucci, L., Langlois, J.A.: Aging successfully until death in old age: opportunities for increasing active life expectancy. Am. J. Epidemiol. 149(7), 654–664 (1999)
Baig, D., Javed, F., Savkin, A.: An adaptive h-infinity control design for exercise-independent human heart rate regulation system. In: 2011 9th IEEE International Conference on Control and Automation (ICCA) (2011)
Steffen, D., Bleser, G., Weber, M., Stricker, D., Fradet, L., Marin, F.: A personalized exercise trainer for elderly. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 24–31 (2011)
Ludwig, M., Sundaram, A.M., Füller, M., Asteroth, A., Prassler, E.: On modeling the cardiovascular system and predicting the human heart rate under strain. In: Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AgingWell) (2015)
Calvert, T., Banister, E.W., Savage, M.V., Bach, T.: A systems model of the effects of training on physical performance. IEEE Trans. Syst. Man Cybern. SMC–6, 94–102 (1976)
Hajek, M., Potucek, J., Brodan, V.: Mathematical model of heart rate regulation during exercise. Automatica 16(2), 191–195 (1980)
Cheng, T.M., Savkin, A.V., Celler, B.G., Wang, L., Su, S.W.: A nonlinear dynamic model for heart rate response to treadmill walking exercise. In: 2007 IEEE International Conference on Engineering in Medicine and Biology Society (EMBS), pp. 2988–2991. IEEE (2007)
Cheng, T., Savkin, A., Celler, B.: Nonlinear modeling and control of human heart rate response during exercise with various work load intensities. IEEE Trans. Biomed. Eng. 55(11), 2499–2508 (2008)
Paradiso, M., Pietrosanti, S., Scalzi, S., Tomei, P., Verrelli, C.: Experimental heart rate regulation in cycle-ergometer exercises. IEEE Trans. Biomed. Eng. 60(1), 135–139 (2013)
Baig, D.Z., Su, H., Cheng, T.M., Savkin, A.V., Su, S.W., Celler, B.G.: Modeling of human heart rate response during walking, cycling and rowing. In: 2010 IEEE International Conference on Engineering in Medicine and Biology (EMBC), pp. 2553–2556. IEEE (2010)
Mohammad, S., Guerra, T.M., Grobois, J.M., Hecquet, B.: Heart rate control during cycling exercise using Takagi-Sugeno models. In: 18th IFAC World Congress. Milano (Italy) (2011)
Su, S., Wang, L., Celler, B., Savkin, A., Guo, Y.: Identification and control for heart rate regulation during treadmill exercise. IEEE Trans. Biomed. Eng. 54(7), 1238–1246 (2007)
Koenig, A., Somaini, L., Pulfer, M.: Model-based heart rate prediction during Lokomat walking. In: Engineering in Medicine and Biology Society, EMBC 2009. Annual International Conference of the IEEE (2009)
Leitner, T., Kirchsteiger, H., Trogmann, H., del Re, L.: Model based control of human heart rate on a bicycle ergometer. In: Control Conference (ECC), 2014 European, pp. 1516–1521. IEEE (2014)
Corno, M., Giani, P., Tanelli, M., Savaresi, S.: Human-in-the-loop bicycle control via active heart rate regulation. IEEE Trans. Control Syst. Technol. 23(3), 1029–1040 (2015)
Afonso, J.A., Rodrigues, F.J., Pedrosa, D., Afonso, J.L.: Automatic control of cycling effort using electric bicycles and mobile devices. In: Proceedings of the World Congress on Engineering 2015. IAENG (2015)
Velikic, G., Modayil, J., Thomsen, M., Bocko, M., Pentland, A.: Predicting the near-future impact of daily activities on heart rate for at-risk populations. In: 13th IEEE International Conference on e-Health Networking Applications and Services (Healthcom), pp. 94–97. IEEE (2011)
Sumida, M., Mizumoto, T., Yasumoto, K.: Estimating heart rate variation during walking with smartphone. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, p. 245. ACM Press (2013)
Wang, L., Su, S.W., Celler, B.G.: Assessing the human cardiovascular response to moderate exercise: feature extraction by support vector regression. Physiol. Meas. 30(3), 227 (2009)
Zhang, Y.: Monitoring, modeling, and regulation for indoor and outdoor exercises, Ph.D. thesis, University of Technology, Sydney (2013)
Yuchi, M., Jo, J.: Heart rate prediction based on physical activity using feedforwad neural network. In: International Conference on Convergence and Hybrid Information Technology, ICHIT 2008, pp. 344–350 (2008)
Xiao, F., Chen, Y., Yuchi, M., Ding, M., Jo, J.: Heart rate prediction model based on physical activities using evolutionary neural network. In: 2010 Fourth International Conference on Genetic and Evolutionary Computing, pp. 198–201. IEEE (2010)
Brzostowski, K., Drapala, J., Grzech, A., Swiatek, P.: Adaptive decision support system for automatic physical effort plan generation - data-driven approach. Cybern. Syst. 44, 204–221 (2013)
Müller, F., Mülller, S., Helmer, A., Hein, A.: Evaluation of a generic heart rate model for exercise planning and execution across training modalities. In: Proceedings of the 7th German AAL Conference (2014)
Lefever, J., Berckmans, D., Aerts, J.M.: Time-variant modelling of heart rate responses to exercise intensity during road cycling. Eur. J. Sport Sci. 14(1), S406–S412 (2014)
Busso, T., Denis, C., Bonnefoy, R., Geyssant, A., Lacour, J.R.: Modeling of adaptations to physical training by using a recursive least squares algorithm. J. Appl. Physiol. 82(5), 1685–1693 (1997)
Hairer, E., Norsett, S., Wanner, G.: Solving Ordinary Differential Equations I. Nonstiff Problems. Springer Series in Computational Mathematics, 2nd edn. Springer, Berlin (1993)
Seal, H.L.: Studies in the history of probability and statistics. XV the historical development of the Gauss linear model. Biometrika 54(1–2), 1–24 (1967)
Tabachnick, B.G., Fidell, L.S.: Using Multivariate Statistics, 5th edn. Allyn & Bacon, Inc., Needham Heights (2006)
Javed, F., Chan, G.S.H., Savkin, A.V., Middleton, P.M., Malouf, P., Steel, E., Mackie, J., Lovell, N.H.: RBF kernel based support vector regression to estimate the blood volume and heart rate responses during hemodialysis. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4352–4355 (2009)
Van Der Malsburg, C.: Frank Rosenblatt: principles of neurodynamics: perceptrons and the theory of brain mechanisms. In: Palm, G., Aertsen, A. (eds.) Brain Theory, pp. 245–248. Springer, Berlin (1986)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)
Acknowledgement
The authors gratefully acknowledge the on-going support of the Bonn-Aachen International Center for Information Technology. Furthermore, the authors would like to thank the subject for his support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Füller, M., Meenakshi Sundaram, A., Ludwig, M., Asteroth, A., Prassler, E. (2015). Modeling and Predicting the Human Heart Rate During Running Exercise. In: Helfert, M., Holzinger, A., Ziefle, M., Fred, A., O'Donoghue, J., Röcker, C. (eds) Information and Communication Technologies for Ageing Well and e-Health. ICT4AWE 2015. Communications in Computer and Information Science, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-27695-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-27695-3_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27694-6
Online ISBN: 978-3-319-27695-3
eBook Packages: Computer ScienceComputer Science (R0)