Performance Evaluation of an IoT-Based E-Learning Testbed Using Mean-Shift Clustering Approach Considering Gamma Type of Brain Waves

  • Masafumi Yamada
  • Miralda Cuka
  • Yi liu
  • Kevin Bylykbashi
  • Keita Matsuo
  • Leonard Barolli
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 12)


Due to the opportunities provided by the Internet, people are taking advantage of e-learning courses and enormous research efforts have been dedicated to the development of e-learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is low. One of the reasons is the low study desire and motivation. In this work, we present an IoT-Based E-Learning testbed using Raspberry Pi mounted on Raspbian. We carried out some experiments with a student of our laboratory for gamma type of brain waves. We used MindWave Mobile (MWM) to get the data and considered four situations: sleeping, relaxing, active and moving. Then, we used mean-shift clustering algorithm to cluster the data. The evaluation results show that our testbed can judge the human situation by using gamma waves.


Internet of Things Testbed Raspberry Pi Raspbian MindWave Mobile Mean-shift Gamma type 


  1. 1.
    Matsuo, K., Barolli, L., Xhafa, F., Kolici, V., Koyama, A., Durresi, A., Miho, R.: Implementation of an E-learning system using P2P, web and sensor technologies. In: Proceedings of IEEE Advanced Information Networking and Applications (AINA-2009), pp. 800–807 (2009)Google Scholar
  2. 2.
    Matsuo, K., Barolli, L., Arnedo-Moreno, J., Xhafa, F., Koyama, A., Durresi, A.: Experimental results and evaluation of SmartBox stimulation device in a P2P E-learning system. In: Proceedings of Network-Based Information Systems (NBiS-2009), pp. 37–44 (2009)Google Scholar
  3. 3.
    Domingo, M.G., Forner, J.A.M.: Expanding the learning environment: combining physicality and virtuality - the internet of things for eLearning. In: Proceedings of 10-th IEEE International Conference on Advanced Learning Technologies (ICALT-2010), pp. 730–731 (2010)Google Scholar
  4. 4.
    Gasparini, I., Eyharabide, V., Schiaffino, S., Pimenta, M.S., Amandi, A., de Oliveira, J.P.M.: Improving user profiling for a richer personalization: modeling context in e-learning. In: Intelligent and Adaptive Learning Systems: Technology Enhanced Support for Learners and Teachers, Chapter 12, pp. 182–197 (2012)Google Scholar
  5. 5.
    de Freitas, V., Marcal, V.P., Gasparini, I., Amaral, M.A., Proenca Jr., M.L., Brunetto, M.A.C., Pimenta, M.S., Ribeiro, C.H.F.P., de Lima, J.V., de Oliveira, J.P.M.: AdaptWeb: an adaptive web-based courseware. In: Proceedings of International Conference on Information and Communication Technologies in Education (ICTE-2002), pp. 131–134 (2002)Google Scholar
  6. 6.
    Schiaffino, S., Garcia, P., Amandi, A.: eTeacher: providing personalized assistance to e-learning students. Comput. Educ. 51(4), 1744–1754 (2008)CrossRefGoogle Scholar
  7. 7.
    Zanella, A., Bui, N., Castellani, A., Vangelista, L.: Internet of Things for smart cities. IEEE Internet Things J. 1(1), 22–32 (2014)CrossRefGoogle Scholar
  8. 8.
    Atzori, L., Iera, A., Morabito, G.: The Internet of Things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)CrossRefzbMATHGoogle Scholar
  9. 9.
    Bellavista, P., Cardone, G., Corradi, A., Foschini, L.: Convergence of MANET and WSN in IoT Urban scenarios. IEEE Sens. J. 13(10), 3558–3567 (2013)CrossRefGoogle Scholar
  10. 10.
    Derpanis, K.G.: Mean Shift Clustering. Accessed 14 Sept 2016
  11. 11.
    Comaniciu, D.: Variable bandwidth density-based fusion. In: Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR-2003), vol. 1, pp. 59–66 (2003)Google Scholar
  12. 12.
    Tuzel, O., Porikli, F., Meer, P.: Kernel methods for weakly supervised mean shift clustering. In: Proceedings of 12-th IEEE International Conference on Computer Vision, pp. 48–55 (2009)Google Scholar
  13. 13.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)CrossRefGoogle Scholar
  14. 14.
    Raspberry Pi Foundation.
  15. 15.
    Oda, T., Barolli, A., Sakamoto, S., Barolli, L., Ikeda, M., Uchida, K.: Implementation and experimental results of a WMN testbed in indoor environment considering LoS scenario. In: Proceedings of 29-th IEEE International Conference on Advanced Information Networking and Applications (AINA-2015), pp. 37–42 (2015)Google Scholar
  16. 16.
    NeuroSky to Release MindWave Mobile.
  17. 17.
    Knyazev, G., et al.: EEG delta oscillations as a correlate of basic homeostatic and motivational processes. Neurosci. Biobehav. Rev. 36(1), 677–695 (2012). ElsevierCrossRefGoogle Scholar
  18. 18.
    Klimesch, W., et al.: EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29(2–3), 169–195 (1999). ElsevierCrossRefGoogle Scholar
  19. 19.
    Teplan, M., et al.: Fundamentals of EGG measurement. Meas. Sci. Rev. 2(2), 1–11 (2002)Google Scholar
  20. 20.
    Vialatte, F.B., Bakardjian, H., Prasad, R., Cichocki, A.: EEG paroxysmal gamma waves during Bhramari Pranayama: a yoga breathing technique. Conscious. Cogn. 18(4), 977–988 (2009). ElesevierCrossRefGoogle Scholar
  21. 21.
    Akin, M.: Comparison of wavelet transform and FFT methods in the analysis of EEG signals. J. Med. Syst. 26(3), 241–247 (2002)CrossRefGoogle Scholar
  22. 22.
    Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(10), 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Masafumi Yamada
    • 1
  • Miralda Cuka
    • 1
  • Yi liu
    • 1
  • Kevin Bylykbashi
    • 2
  • Keita Matsuo
    • 3
  • Leonard Barolli
    • 3
  1. 1.Graduate School of EngineeringFukuoka Institute of Technology (FIT)FukuokaJapan
  2. 2.Faculty of Information TechnologyPolytechnic University of TiranaTiranaAlbania
  3. 3.Department of Information and Communication EngineeringFukuoka Institute of Technology (FIT)FukuokaJapan

Personalised recommendations