[Abstract + References] Neuromotor Recovery Based on BCI, FES, Virtual Reality and Augmented Feedback for Upper Limbs

Abstract

Recently investigated rehabilitative practices involving Brain-Computer Interface (BCI) and Functional Electrical Stimulation (FES) techniques provided long-lasting benefits after short-term recovering programs. The prevalence of this revolutionary approach received a boost from virtual reality and augmented reality, which contribute to the brain neuroplasticity improvement and can be used in neurorehabilitation and treatment of motor/mental disorders. This work presents a therapy system for stroke rehabilitation based on these techniques. The novelty of the proposed system consists of including an eye tracking device that detects the patient’s vigilance during exercises and warns if patient is not focused on the items of interest from the virtual environment. This additional feature improves the level of user involvement and makes him/her conscious of the rehabilitation importance and pace. Moreover, the system architecture is reconfigurable, and the functionalities are specified by software. The laboratory tests have validated the system from a technical point of view, and preliminary results from the clinical tests have highlighted the system’s quick accommodation to the proposed therapy and fast progress for each user.This is a preview of subscription content, log in to check access.

References

  1. 1.ENIGMA-Stroke Recovery. http://enigma.ini.usc.edu/ongoing/enigma-stroke-recovery/. Last visit October 2018
  2. 2.Johns Hopkins Institute—Strock Centers. www.hopkinsmedicine.org/neurology_neurosurgery/centers_clinics/cerebrovascular/stroke/. Last visit December 2018
  3. 3.StrokeBack Project. http://www.strokeback.eu/project.html. Last visit December 2018
  4. 4.NIHR—A practical, yet flexible functional electrical stimulation system for upper limb functional rehabilitation, Centres for Health Sciences Research, 2014–2017. https://www.salford.ac.uk/research/health-sciences/research-groups/human-movement-technologies/a-practical,-yet-flexible-functional-electrical-stimulation-system-for-upper-limb-functional-rehabilitation. Last visit December 2018
  5. 5.RETRAINER. http://www.retrainer.eu/start/. Last visit December 2018
  6. 6.C.M. McCrimmon, C.E. King, P.T. Wang, S.C. Cramer, Z. Nenadic, A.H. Do, Brain-controlled functional electrical stimulation for lower-limb motor recovery in stroke survivors, in 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1247–1250, 2014Google Scholar
  7. 7.M. Sun, C. Smith, D. Howard, L. Kenney, H. Luckie, K. Waring, P. Taylor, E. Merson, S. Finn, FES-UPP: a flexible functional electrical stimulation system to support upper limb functional activity practice. Front Neurosci. 12, 449 (2018)CrossRefGoogle Scholar
  8. 8.O. Ferche, A. Moldoveanu, F. Moldoveanu, The TRAVEE system for neuromotor recovery: Architecture and implementation, in 2017 E-Health and Bioengineering Conference (EHB), Sinaia, 2017, pp. 575–578. https://doi.org/10.1109/EHB.2017.7995489Google Scholar
  9. 9.S. Caraiman, A. Stan, N. Botezatu, P. Herghelegiu, R.G. Lupu, A. Moldoveanu, Architectural design of a real-time augmented feedback system for neuromotor rehabilitation, in 2015 20th International Conference on Control Systems and Computer Science, Bucharest, 2015, pp. 850–855.  https://doi.org/10.1109/cscs.2015.106
  10. 10.R.G. Lupu et al., Virtual reality system for stroke recovery for upper limbs using ArUco markers, in 2017 21st International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, 2017, pp. 548–552,  https://doi.org/10.1109/icstcc.2017.8107092
  11. 11.R.G. Lupu, N. Botezatu, F. Ungureanu, D. Ignat, A. Moldoveanu, Virtual reality based stroke recovery for upper limbs using Leap Motion, in 2016 20th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, 2016, pp. 295–299.  https://doi.org/10.1109/icstcc.2016.7790681
  12. 12.R.G. Lupu, D.C. Irimia, F. Ungureanu, M.S. Poboroniuc, A. Moldoveanu, BCI and FES based therapy for stroke rehabilitation using VR facilities. Wireless Commun. Mob. Comput. (2018)Google Scholar
  13. 13.D.C. Irimia, M.S. Poboroniuc, R. Ortner, B.Z. Allison, C. Guger, Preliminary results of testing a BCI-controlled FES system for post-stroke rehabilitation, in Proceedings of the 7th Graz Brain-Computer Interface Conference 2017, September 18th–22nd, Graz, Austria, 2017Google Scholar
  14. 14.D.C. Irimia, R. Ortner, M.S. Poboroniuc, B.E. Ignat, C. Guger, High classification accuracy of a motor imagery based brain-computer interface for stroke rehabilitation training. Front. Rob. AI 5, 130 (2018)CrossRefGoogle Scholar
  15. 15.S. Lemm, B. Blankertz, T. Dickhaus, K.-R. Müller, Introduction to machine learning for brain imaging. NeuroImage 56(2), 387–399 (2011)CrossRefGoogle Scholar
  16. 16.J. Müller-Gerking, G. Pfurtscheller, H. Flyvbjerg, Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin. Neurophysiol. 110(5), 787–798 (1999)CrossRefGoogle Scholar
  17. 17.C.L. Watkins, M.J. Leathley, J.M. Gregson, A.P. Moore, T.L. Smith, A.K. Sharma, Prevalence of spasticity post stroke. Clin. Rehab. (2002).  https://doi.org/10.1191/0269215502cr512oaCrossRefGoogle Scholar
  18. 18.D.A. De Silva, N. Venketasubramanian, A. Jr. Roxas, L.P. Kee, Y. Lampl, Understanding Stroke—A Guide for Stroke Survivors and Their Families, 2014. http://www.moleac.com/ebook/Understanding_Stroke_-_Guide_for_Stroke_Survivors.pdf

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