Abstract
It is necessary to develop smart e-learning systems that can evaluate in real time not only student’s knowledge, skills and experience, but also his functional state. The learning load and intensity should not lead to a reduction of student’s functional state, including learner’s mental working capacity. Student’s functional state can be evaluated by analysis of heart rate variability, since heart rhythm responds to all changes in the human body and environment. There are a lot of devices for measuring heart rate variability, which called heart rate monitors. In massive e-learning more accessible monitors should be used but such monitors may not be sufficiently accurate. This paper studies three devices that can be used to estimate student’s mental working capacity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Uskov, V., Lyamin, A., Lisitsyna, L., Sekar, B.: Smart e-learning as a student-centered biotechnical system. In: Vincenti, G., Bucciero, A., Vaz de Carvalho, C. (eds.) eLEOT 2014. LNICST, vol. 138, pp. 167–175. Springer, Heidelberg (2014)
Lisitsyna, L., Lyamin, A., Skshidlevsky, A.: Estimation of student functional state in learning management system by heart rate variability method. In: Neves-Silva, R., Tsihrintzis, G.A., Uskov, V., Howlett, R.J., Jain, L.C. (eds.) Smart Digital Futures 2014 - Frontiers in Artificial Intelligence and Applications, vol. 262, pp. 726–731. IOS Press (2014)
Wu, W., Lee, J.: Improvement of HRV methodology for positive/negative emotion assessment. In: Proceedings of the 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing, Washington, pp. 1–6 (2009)
Maraes, V., Carreiro, D., Barbosa, N.: Study of heart rate variability of university trained at rest and exercise. In: 2013 Pan American Health Care Exchanges (PAHCE), Medellin, pp. 1–5 (2013)
Denison, T., Morris, M., Sun, F.: Building a bionic nervous system. IEEE Spectr. 52(2), 32–39 (2015). IEEE, New York
Silipo, R., Banolan, G.: Neural and traditional techniques in diagnostic ECG classification. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Munich, vol. 1, pp. 123–126 (1997)
Biel, L., Pettersson, O., Philipson, L., Wide, P.: ECG analysis: a new approach in human identification. IEEE Trans. Instrum. Measur. 50(3), 808–812 (2001). IEEE, New York
Heart rate variability. standards of measurement, physiological interpretation and clinical use. Eur. Heart J. 17, 354–381 (1996). Oxford University Press
Uskov, V., Uskova, M.: Reusable learning objects approach to web-based education. In: Proceedings of the 5th International Conference on Computers and Advanced Technology in Education CATE-2002, 20–22 May 2002, Cancun, Mexico, pp. 165–170 (2002)
Acknowledgement
This paper is supported by Government of Russian Federation (grant 074-U01).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Berdnikova, E., Lyamin, A., Skshidlevsky, A. (2016). Analysis of Heart Rate Monitors for Evaluating Student’s Mental Working Capacity. In: Gong, Z., Chiu, D., Zou, D. (eds) Current Developments in Web Based Learning. ICWL 2015. Lecture Notes in Computer Science(), vol 9584. Springer, Cham. https://doi.org/10.1007/978-3-319-32865-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-32865-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32864-5
Online ISBN: 978-3-319-32865-2
eBook Packages: Computer ScienceComputer Science (R0)