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

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Big Data for the Greater Good

Part of the book series: Studies in Big Data ((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.

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Notes

  1. 1.

    The corresponding trajectory is called the statokinesiogram; it is the map of the COP in the anteroposterior direction versus the COP in the mediolateral direction [25].

  2. 2.

    In different medical fields, the term proband is often used to denote a particular subject, e.g., person or animal, being studied or reported on.

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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)”.

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Authors and Affiliations

  1. Joint Department of Biomedical Engineering, Department of Rehabilitation Medicine, First Faculty of Medicine of CU and General Teaching Hospital in Prague, CTU and Charles University (CU), Prague, Czech Republic

    Marketa Janatova

  2. Robotics and Machine Perception Department, CIIRC CTU, Prague, Czech Republic

    Miroslav Uller

  3. Biomedical Engineering and Assistive Technologies Department, CIIRC CTU, Prague, Czech Republic

    Olga Stepankova

  4. Faculty of Computer Science, University of Vienna, Vienna, Austria

    Peter Brezany & Marek Lenart

  5. SIX Research Group, Brno University of Technology, Brno, Czech Republic

    Peter Brezany

Authors
  1. Marketa Janatova
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  2. Miroslav Uller
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  3. Olga Stepankova
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  4. Peter Brezany
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  5. Marek Lenart
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Corresponding author

Correspondence to Peter Brezany .

Editor information

Editors and Affiliations

  1. Operations and Information Management, Aston Business School, Aston University, Birmingham, United Kingdom

    Ali Emrouznejad

  2. Buckingham Business School, University of Buckingham, Buckingham, United Kingdom

    Vincent Charles

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Janatova, M., Uller, M., Stepankova, O., Brezany, P., Lenart, M. (2019). A Novel Big Data-Enabled Approach, Individualizing and Optimizing Brain Disorder Rehabilitation. In: Emrouznejad, A., Charles, V. (eds) Big Data for the Greater Good. Studies in Big Data, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-319-93061-9_5

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