Advertisement

A Novel Big Data-Enabled Approach, Individualizing and Optimizing Brain Disorder Rehabilitation

  • Marketa Janatova
  • Miroslav Uller
  • Olga Stepankova
  • Peter Brezany
  • Marek Lenart
Chapter
Part of the Studies in Big Data book series (SBD, volume 42)

Abstract

Brain disorders occur when our brain is damaged or negatively influenced by injury, surgery, or health conditions. This chapter shows how the combination of novel biofeedback-based treatments producing large data sets with Big Data and Cloud-Dew Computing paradigms can contribute to the greater good of patients in the context of rehabilitation of balance disorders, a significant category of brain damage impairments. The underlying hypothesis of the presented original research approach is that detailed monitoring and continuous analysis of patient´s physiological data integrated with data captured from other sources helps to optimize the therapy w.r.t. the current needs of the patient, improves the efficiency of the therapeutic process, and prevents patient overstressing during the therapy. In the proposed application model, training built upon two systems, Homebalance—a system enabling balance training and Scope—a system collecting physiological data, is provided both in collaborating rehabilitation centers and at patient homes. The preliminary results are documented using a case study confirming that the approach offers a viable way towards the greater good of a patient.

Keywords

Brain damage Biofeedback Data analysis Dew computing 

Notes

Acknowledgements

The work described in this chapter has been carried out as part of three projects, namely, research grants SGS16/231/OHK3/3T/13 “Support of interactive approaches to biomedical data acquisition and processing” and SGS17/206/OHK4/3T/17 “Complex monitoring of the patient during the virtual reality based therapy” provided by the Czech Technical University in Prague and the Czech National Sustainability Program supported by grant LO1401 “Advanced Wireless Technologies for Clever Engineering (ADWICE)”.

References

  1. 1.
    J. Bakker, M. Pechenizkiy, N. Sidorova, What’s your current stress level? Detection of stress patterns from GSR sensor data, in Data Mining Workshops IEEE 11th International Conference (2011), pp. 573–580Google Scholar
  2. 2.
    A. Bohuncak, M. Ticha, M. Janatova, Comparative study of two stabilometric platforms for the application in 3D biofeedback system in Abstracts of the 6th international posture symposium, p. 21Google Scholar
  3. 3.
    A. Bohuncak, M. Janatova, M. Ticha, O. Svestkova, K. Hana, Development of interactive rehabilitation devices, in Smart Homes (2012), pp. 29–31Google Scholar
  4. 4.
    N.A. Borghese, M. Pirovano, P.L. Lanzi, S. Wüest, E.D. de Bruin, Computational intelligence and game design for effective at-home stroke rehabilitation. Games Health: Res. Dev. Clin. Appl. 2(2), 81–88 (2013)CrossRefGoogle Scholar
  5. 5.
    O. Cakrt et al., Balance rehabilitation therapy by tongue electrotactile biofeedback in patients with degenerative cerebellar disease. NeuroRehabilitation 31(4), 429–434 (2012)Google Scholar
  6. 6.
    K.H. Cho, K.J. Lee, C.H. Song, Virtual-reality balance training with a video-game system improves dynamic balance in chronic stroke patients. Tohoku J. Exp. Med. 228(1), 69–74 (2012)CrossRefGoogle Scholar
  7. 7.
    R. Dörner, S. Göbel, Serious Games: Foundations: Concepts and Practice (Springer, Cham, 2016), p. 2016CrossRefGoogle Scholar
  8. 8.
    I. Elsayed, Dataspace support platform for e-science. Ph.D. thesis, Faculty of Computer Science, University of Vienna, 2011. Supervised by P. Brezany, Revised version published by Südwestdeutscher Verlag für Hochschulschriften (https://www.svh-verlag.de/), 2013. ISBN: 978-3838131573, 2013
  9. 9.
    J.-F. Esculier et al., Home-based balance training programme using WiiFit with balance board for Parkinson’s disease: a pilot study. J. Rehabil. Med. 44, 144–150 (2012)CrossRefGoogle Scholar
  10. 10.
    M. Ferreira, A. Carreiro, A. Damasceno, Gesture analysis algorithms. Procedia Technol. 9, 1273–1281 (2013)CrossRefGoogle Scholar
  11. 11.
    Force Platform (2016), https://en.wikipedia.org/wiki/Force_platform. Accessed 12 Nov 2016
  12. 12.
    V. Gatica-Rojas, G. Méndez-Rebolledo, Virtual reality interface devices in the reorganization of neural networks in the brain of patients with neurological diseases. Neural Regeneration Res. 9(8), 888–896 (2014)CrossRefGoogle Scholar
  13. 13.
    O.M. Giggins, U.M. Persson, B. Caulfield, Biofeedback in rehabilitation. J. Neuroeng. Rehabil. 10(1), 60 (2013)CrossRefGoogle Scholar
  14. 14.
    J.A. Gil-Gómez, R. Lloréns, M. Alcañiz, C. Colomer, Effectiveness of a Wii balance board-based system (eBaViR) for balance rehabilitation: a pilot randomized clinical trial in patients with acquired brain injury. J. Neuroeng. Rehabil. 8(1), 30 (2011)CrossRefGoogle Scholar
  15. 15.
    M. Janatová, M. Tichá, M. Gerlichová et al., Terapie poruch rovnováhy u pacientky po cévní mozkové příhodě s využitím vizuální zpětné vazby a stabilometrické plošiny v domácím prostředí. Rehabilitácia 52(3), 140–146 (2015)Google Scholar
  16. 16.
    K. Keahey, M. Tsugawa, A. Matsunaga, J. Fortes, Sky computing. IEEE Internet Comput. 13(2009), 43–51 (2009)CrossRefGoogle Scholar
  17. 17.
    B.B. Lahiri, S. Bagavathiappan, T. Jayakumar, J. Philip, Medical applications of infrared thermography: a review. Infrared Phys. Technol. 55(4), 221–235 (2012)CrossRefGoogle Scholar
  18. 18.
    V. Mayer-Schonberger, K. Cukier, Big Data: A Revolution That Will Transform How We Live, Work and Think (John Murray (Publishers), London, 2013)Google Scholar
  19. 19.
    T. O’Donovan, J. O’Donoghue, C. Sreenan, P. O’Reilly, D. Sammon, K. O’Connor, A context aware wireless body area network (BAN), in Proceedings of the Pervasive Health Conference (2009)Google Scholar
  20. 20.
    M. Oliver, et al., in Smart Computer-Assisted Cognitive Rehabilitation for the Ageing Population, Ambient Intelligence-Software and Applications—7th International Symposium on Ambient Intelligence, vol. 476 of the Series Advances in Intelligent Systems and Computing (2016), pp. 197–205CrossRefGoogle Scholar
  21. 21.
    J. Pan, J.W. Tompkins, A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME- 32(3), 230–236 (1985)CrossRefGoogle Scholar
  22. 22.
    PMML, (2016), http://dmg.org/pmml/v4-3/TimeSeriesModel.html. Accessed 25 Feb 2017
  23. 23.
  24. 24.
    K. Skala, D. Davidovic, E. Afgan, I. Sovic, Z. Sojat, Scalable distributed computing hierarchy: cloud, fog and dew computing. Open J. Cloud Comput. (OJCC), 2(1), 16–24 (2015)Google Scholar
  25. 25.
  26. 26.
    F. Sun, C. Kuo, H. Cheng, S. Buthpitiya, P. Collins, M. Griss, in Activity-Aware Mental Stress Detection Using Physiological Sensors. Lecture Notes of the Institute for Computer Sciences. Social Informatics and Telecommunications Engineering Mobile Computing, Applications, and Services (2012), pp. 211–230Google Scholar
  27. 27.
    J. Sweller, P. Ayres, S. Kalyuga: Cognitive Load Theory, Springer Science & Business Media, (2011)CrossRefGoogle Scholar
  28. 28.
    M. Tichá, M. Janatová, R. Kliment, O. Švestková, K. Hána, Mobile rehabilitation device for balance training with visual feedback, in Proceedings of International Conference on Mobile and Information Technologies in Medicine and Health (2014), pp. 22–24Google Scholar
  29. 29.
    Y. Wang, Cloud-dew architecture. Int. J. Cloud Comput. 4(3), 199–210 (2015a)CrossRefGoogle Scholar
  30. 30.
    Y. Wang, The initial definition of dew computing. Dew Comput. Res. (2015b)Google Scholar
  31. 31.
    Y. Wang, Definition and categorization of dew computing. Open J. Cloud Comput. (OJCC), 3(1), 1–7 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Marketa Janatova
    • 1
  • Miroslav Uller
    • 2
  • Olga Stepankova
    • 3
  • Peter Brezany
    • 4
    • 5
  • Marek Lenart
    • 4
  1. 1.Joint Department of Biomedical Engineering, Department of Rehabilitation Medicine, First Faculty of Medicine of CU and General Teaching Hospital in PragueCTU and Charles University (CU)PragueCzech Republic
  2. 2.Robotics and Machine Perception DepartmentCIIRC CTUPragueCzech Republic
  3. 3.Biomedical Engineering and Assistive Technologies DepartmentCIIRC CTUPragueCzech Republic
  4. 4.Faculty of Computer ScienceUniversity of ViennaViennaAustria
  5. 5.SIX Research GroupBrno University of TechnologyBrnoCzech Republic

Personalised recommendations