Advanced Hybrid Technology for Neurorehabilitation: The HYPER Project

  • Alessandro De Mauro
  • Eduardo Carrasco
  • David Oyarzun
  • Aitor Ardanza
  • Anselmo Frizera-Neto
  • Diego Torricelli
  • José Luis Pons
  • Angel Gil Agudo
  • Julian Florez
Part of the Intelligent Systems Reference Library book series (ISRL, volume 26)

Abstract

Disabilities that follow cerebrovascular accidents and spinal cord injuries severely impair motor functions and thereby prevent the affected individuals from full and autonomous participation in activities of daily living. Rehabilitation therapy is needed in order to recover from those severe physical traumas. Where rehabilitation is not enough to restore completely human functions then functional compensation is required. In the last years the field of rehabilitation has been inspired by new available technologies. An example is given by rehabilitation robotics where machines are used to assist the patient in the execution of specific and physical task of the therapy. In both rehabilitation and functional compensation scenarios, the usability and cognitive aspects of human-machine interaction have yet to be solved efficiently by robotic-assisted solutions. Hybrid systems combining exoskeletal robots (ERs) with motor neuroprosthesis (MNPs) emerge as promising techniques that blends together technologies that could overcome the limitations of each individual one. Another promising technology which is rapidly becoming a popular application for physical rehabilitation and motor control research is Virtual Reality (VR). In this chapter, we present our research focuses on the development of a new rehabilitation therapy based on an integrated ER-MNP hybrid systems combined with virtual reality and brain neuro-machine interface (BNMI). This solution, based on improved cognitive and physical human-machine interaction, aims to overcome the major limitations regarding the current available robotic-based therapies.

Keywords

Spinal Cord Injury Virtual Reality Functional Electrical Stimulation Rehabilitation Therapy Body Weight Support 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Boian, R., Lee, C., Deutsch, J., Burdea, G., Lewis, J.: Virtual reality-based system for ankle rehabilitation post stroke. In: Proceedings of the First International Workshop on Virtual Reality Rehabilitation, Citeseer, pp. 77–86 (2002)Google Scholar
  5. 5.
    Colombo, G., Jrg, M., Dietz, V.: Driven gait orthosis to do locomotor training of paraplegic patients, pp. 3159–3163 (2000)Google Scholar
  6. 6.
    Colombo, R., Pisano, F., Mazzone, A., Delconte, C., Micera, S., Carrozza, M., Dario, P., Minuco, G.: Design strategies to improve patient motivation during robot-aided rehabilitation. Journal of Neuro Engineering and Rehabilitation 4 (2007)Google Scholar
  7. 7.
    Cano de la Cuerda, R., Muñoz-Hellín, E., Algual-Diego, I.M., Molina-Rueda, F.: Telerrehabilitación y neurología. Rev. Neurol. 51, 49–56 (2010)Google Scholar
  8. 8.
    Dietz, V.: Spinal cord pattern generators for locomotion. Clinical Neurophysiology 114(8), 1379–1389 (2003)CrossRefGoogle Scholar
  9. 9.
    Dobkin, B.: Rehabilitation after stroke. New England Journal of Medicine 352(16), 1677–1684 (2005)CrossRefGoogle Scholar
  10. 10.
    Eilenberg, M.F., Geyer, H., Herr, H.: Control of a powered ankle-foot prosthesis based on a neuromuscular model. IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society 18(2), 164–173 (2010), http://www.ncbi.nlm.nih.gov/pubmed/20071268, doi:10.1109/TNSRE.2009.2039620CrossRefGoogle Scholar
  11. 11.
    Emken, J., Bobrow, J.E., Reinkensmeyer, D.: Robotic movement training as an optimization problem: Designing a controller that assists only as needed, pp. 307–312 (2005)Google Scholar
  12. 12.
    Fidopiastis, C., Stapleton, C., Whiteside, J., Hughes, C., Fiore, S., Martin, G., Rolland, J., Smith, E.: Human experience modeler: Context-driven cognitive retraining to facilitate transfer of learning. CyberPsychology & Behavior 9(2), 183–187 (2006)CrossRefGoogle Scholar
  13. 13.
    Fugl-Meyer, A., Jääskö, L., Leyman, I., Olsson, S., Steglind, S.: The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scandinavian journal of rehabilitation medicine 7(1), 13–31 (1975)Google Scholar
  14. 14.
    Geng, T., Porr, B., Wörgötter, F.: A reflexive neural network for dynamic biped walking control. Neural Computation 18(5), 1156–1196 (2006)MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Giszter, S.: Spinal cord injury: Present and future therapeutic devices and prostheses. Neurotherapeutics 5(1), 147–162 (2008)CrossRefGoogle Scholar
  16. 16.
    Hesse, S.: Locomotor therapy in neurorehabilitation. NeuroRehabilitation 16(3), 133–139 (2001)Google Scholar
  17. 17.
    Holden, M.: Virtual environments for motor rehabilitation: review. Cyberpsychology & behavior 8(3), 187–211 (2005)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Jezernik, S., Colombo, G., Keller, T., Frueh, H., Morari, M.: Robotic orthosis lokomat: A rehabilitation and research tool. Neuromodulation 6(2), 108–115 (2003)CrossRefGoogle Scholar
  19. 19.
    Jo, S., Massaquoi, S.: A model of cerebrocerebello-spinomuscular interaction in the sagittal control of human walking. Biological Cybernetics 96(3), 279–307 (2007)MATHCrossRefGoogle Scholar
  20. 20.
    Johansson, B.: Brain plasticity and stroke rehabilitation: The willis lecture. Stroke 31(1), 223–230 (2000)CrossRefGoogle Scholar
  21. 21.
    Kazerooni, H.: Human-robot interaction via the transfer of power and information signals. IEEE Transactions on Systems, Man and Cybernetics 20(2), 450–463 (1990)CrossRefGoogle Scholar
  22. 22.
    Knaut, L., Subramanian, S., McFadyen, B., Bourbonnais, D., Levin, M.: Kinematics of pointing movements made in a virtual versus a physical 3-dimensional environment in healthy and stroke subjects. Arch. Phys. Med. Rehabil. 90, 793–802 (2009)CrossRefGoogle Scholar
  23. 23.
    Koenig, A., Wellner, M., Köneke, S., Meyerheim, A., Lunenburger, L., Riener, R.: Virtual gait training for children with cerebral palsy using the lokomat gait orthosis. Medicine meets virtual reality 16, 204 (2008)Google Scholar
  24. 24.
    Kuttuva, M., Boian, R., Merians, A., Burdea, G., Bouzit, M., Lewis, J., Fensterheim, D.: The rutgers arm: an upper-extremity rehabilitation system in virtual reality. In: 4th International workshop on virtual reality rehabilitation, Catalina Islands. Citeseer (2005)Google Scholar
  25. 25.
    Lam, T., Wolfe, D., Eng, J., Domingo, A.: Lower limb rehabilitation following spinal cord injury. In: Eng, J., Teasell, R., Miller, W., Wolfe, D., Townson, A., Hsieh, J., Connolly, S., Mehta, S., Sakakibara, B. (eds.) Spinal Cord Injury Rehabilitation Evidence, Version 3.0, pp. 1–47 (2010)Google Scholar
  26. 26.
    Lécuyer, A., Lotte, F., Reilly, R.B., Leeb, R., Hirose, M., Slater, M.: Brain-computer interfaces, virtual reality, and videogames. Computer 41, 66–72 (2008)CrossRefGoogle Scholar
  27. 27.
    Luksch, T.: Human-like control of dynamically walking bipedal robots. Ph.D. thesis, Technischen Universitat Kaiserslautern (2009)Google Scholar
  28. 28.
    Lunenburger, L., Colombo, G., Riener, R.: Biofeedback for robotic gait rehabilitation. Journal of Neuro Engineering and Rehabilitation 4 (2007)Google Scholar
  29. 29.
    MacKay-Lyons, M.: Central pattern generation of locomotion: A review of the evidence. Physical Therapy 82(1), 69–83 (2002)Google Scholar
  30. 30.
    Maclean, N., Pound, P., Wolfe, C., Rudd, A.: Qualitative analysis of stroke patients’ motivation for rehabilitation. British Medical Journal 321(7268), 1051–1054 (2000)CrossRefGoogle Scholar
  31. 31.
    Metrailler, P., Brodard, R., Stauffer, Y., Clavel, R., Frischknecht, R.: Cyberthosis: Rehabilitation robotics with controlled electrical muscle stimulation. In: Rehabilitation robotics, pp. 648–664. Itech Education and Publishing, Austria (2007)Google Scholar
  32. 32.
    Mirelman, A., Bonato, P., Deutsch, J.: Effects of training with a robot-virtual reality system compared with a robot alone on the gait of individuals after stroke. Stroke 40(1), 169–174 (2009)CrossRefGoogle Scholar
  33. 33.
    O’Dell, M., Lin, C.C., Harrison, V.: Stroke rehabilitation: Strategies to enhance motor recovery. Annual Review of Medicine 60, 55–68 (2009)CrossRefGoogle Scholar
  34. 34.
    Ogihara, N., Yamazaki, N.: Generation of human bipedal locomotion by a bio-mimetic neuro-musculo-skeletal model. Biol. Cybern. 84(1), 1–11 (2001)CrossRefGoogle Scholar
  35. 35.
    Paul, C., Bellotti, M., Jezernik, S., Curt, A.: Development of a human neuro-musculo-skeletal model for investigation of spinal cord injury. Biol. Cybern. 93(3), 153–170 (2005), http://dx.doi.org/10.1007/s00422-005-0559-x, doi:10.1007/s00422-005-0559-xMATHCrossRefGoogle Scholar
  36. 36.
    Pfurtscheller, G., Leeb, R., Faller, J., Neuper, C.: Brain-computer interface systems used for virtual reality control. In: Kim, J.-J. (ed.) Virtual Reality (2011)Google Scholar
  37. 37.
    Pons, J.: Rehabilitation exoskeletal robotics. IEEE Engineering in Medicine and Biology Magazine 29(3), 57–63 (2010)CrossRefGoogle Scholar
  38. 38.
    Pons, J., Corporation, E.: Wearable robots: biomechatronic exoskeletons. Wiley Online Library, Chichester (2008)CrossRefGoogle Scholar
  39. 39.
    Riener, R., Lïenburger, L., Jezernik, S., Anderschitz, M., Colombo, G., Dietz, V.: Patient-cooperative strategies for robot-aided treadmill training: First experimental results. IEEE Transactions on Neural Systems and Rehabilitation Engineering 13(3), 380–394 (2005)CrossRefGoogle Scholar
  40. 40.
    Rizzo, A., Kin, G.: A swot analysis of the field of vr rehabilitation and therapy. Presence: Teleoperators and Virtual Environments 14(2), 119–146 (2005)CrossRefGoogle Scholar
  41. 41.
    Rose, F., Attree, E., Brooks, B.: Virtual environments in neuropsychological assessment and rehabilitation. IOS Press, Amsterdam (1997)Google Scholar
  42. 42.
    Schmidt, H., Hesse, S., Bernhardt, R., Krüger, J.: Hapticwalker. a novel haptic foot device. ACM Transactions on Applied Perception (TAP) 2(2), 166–180 (2005)CrossRefGoogle Scholar
  43. 43.
    Schmidt, H., Sorowka, D., Piorko, F., Marhoul, N., Bernhardt, R.: Control system for a robotic walking simulator. In: Proceedings. 2004 IEEE International Conference on Robotics and Automation, vol. 2, pp. 2055–2060. IEEE, Los Alamitos (2004)Google Scholar
  44. 44.
    Schmitt, C., Metrailler, P., Al-Khodairy, A., Brodard, R., Fournier, J., Bouri, M., Clavel, R.: The motion maker: a rehabilitation system combining an orthosis with closed-loop electrical muscle stimulation. In: Proceedings of the 8th Vienna International Workshop in Functional Electrical Stimulation, pp. 117–120 (2004)Google Scholar
  45. 45.
    Schmitt, C., Metrailler, P., Al-Khodairy, A., Brodard, R., Fournier, J., Bouri, M., Clavel, R.: A study of a knee extension controlled by a closed loop functional electrical stimulation. In: Proceedings, 9th An. conf. of the IFESS, Bournemouth (2004)Google Scholar
  46. 46.
    Shan, J., Nagashima, F.: Neural locomotion controller design and implementation for humanoid robot hoap-1. In: 20th Annual Conference of the Robotics Society of Japan (2002)Google Scholar
  47. 47.
    Stauffer, Y., Allemand, Y., Bouri, M., Fournier, J., Clavel, R., Metrailler, P., Brodard, R., Reynard, F.: The walktrainer a new generation of walking reeducation device combining orthoses and muscle stimulation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 17(1), 38–45 (2009)CrossRefGoogle Scholar
  48. 48.
    Subramanian, S., Knaut, L., Beaudoin, C., McFadyen, B., Feldman, A., Levin, M.: Virtual reality environments for post-stroke arm rehabilitation. Journal of Neuro Engineering and Rehabilitation 4(1), 20 (2007)CrossRefGoogle Scholar
  49. 49.
    Sveistrup, H.: Motor rehabilitation using virtual reality. Journal of NeuroEngineering and Rehabilitation 1(1), 10 (2004)CrossRefGoogle Scholar
  50. 50.
    Taga, G.: A model of the neuro-musculo-skeletal system for human locomotion. i. emergence of basic gait. Biol. Cybern. 73(2), 97–111 (1995)MATHCrossRefGoogle Scholar
  51. 51.
    Veneman, J., Kruidhof, R., Hekman, E., Ekkelenkamp, R., Van Asseldonk, E., Van Der Kooij, H.: Design and evaluation of the lopes exoskeleton robot for interactive gait rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 15(3), 379–386 (2007)CrossRefGoogle Scholar
  52. 52.
    Viau, A., Feldman, A., McFadyen, B., Levin, M.: Reaching in reality and virtual reality: a comparison of movement kinematics in healthy subjects and in adults with hemiparesis. Journal of neuroengineering and rehabilitation 1(1), 11 (2004)CrossRefGoogle Scholar
  53. 53.
    Weiss, A., Suzuki, T., Bean, J., Fielding, R.: High intensity strength training improves strength and functional performance after stroke. American Journal of Physical Medicine and Rehabilitation 79(4), 369–376 (2000)CrossRefGoogle Scholar
  54. 54.
    Weiss, P., Kizony, R., Feintuch, U., Katz, N.: Virtual reality in neurorehabilitation. Textbook of Neural Repair and Neurorehabilitation 2, 182–197 (2006)CrossRefGoogle Scholar
  55. 55.
    Wolbrecht, E., Chan, V., Reinkensmeyer, D., Bobrow, J.: Optimizing compliant, model-based robotic assistance to promote neurorehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 16(3), 286–297 (2008)CrossRefGoogle Scholar
  56. 56.
    Wolfe, D., Hsieh, J., Mehta, S.: Rehabilitation practices and associated outcomes following spinal cord injury. In: Eng, J., Teasell, R., Miller, W., Wolfe, D., Townson, A., Hsieh, J., Connolly, S., Mehta, S., Sakakibara, B. M. (eds.) Spinal Cord Injury Rehabilitation Evidence, Version 3.0 (2010)Google Scholar
  57. 57.
    Wool, R., Siegel, D., Fine, P.: Task performance in spinal cord injury: effect of helplessness training. Archives of Physical Medicine and Rehabilitation 61(7), 321–325 (1980)Google Scholar
  58. 58.
    You, S., Jang, S., Kim, Y., Hallett, M., Ahn, S., Kwon, Y., Kim, J., Lee, M.: Virtual reality-induced cortical reorganization and associated locomotor recovery in chronic stroke: an experimenter-blind randomized study. Stroke 36(6), 1166–1171 (2005)CrossRefGoogle Scholar

Copyright information

© IFIP 2012

Authors and Affiliations

  • Alessandro De Mauro
    • 1
  • Eduardo Carrasco
    • 1
  • David Oyarzun
    • 1
  • Aitor Ardanza
    • 1
  • Anselmo Frizera-Neto
    • 2
  • Diego Torricelli
    • 2
  • José Luis Pons
    • 2
  • Angel Gil Agudo
    • 3
  • Julian Florez
    • 1
  1. 1.eHealth and Biomedical DepartmentVICOMTechSan SebastianSpain
  2. 2.Bioengineering GroupCSICMadridSpain
  3. 3.Biomechanics UnitNational Hospital of ParaplegicsToledoSpain

Personalised recommendations