Advertisement

Multimodal Interfaces to Improve Therapeutic Outcomes in Robot-Assisted Rehabilitation

  • Loredana ZolloEmail author
  • Eugenia Papaleo
  • Luca Spedaliere
  • Eugenio Guglielmelli
  • Francisco Javier Badesa
  • Ricardo Morales
  • Nicolas Garcia-Aracil
Conference paper
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 94)

Abstract

This chapter intends to provide a description of the MAAT experiment in the framework of the ECHORD European project. The experiment is aimed at developing a novel robotic system for upper-limb rehabilitation, capable of maximizing patient motivation and involvement in the therapy and performing a continuous assessment of the progress of the patient recovery in a multimodal way. The key-issue of the MAAT approach is to include the patient in the control loop by means of multimodal patient data (biomechanical as well as physiological data) and an immersive virtual reality system. To this purpose, a bio-cooperative controller is developed that incorporates multimodal data and adaptively and dynamically change the complexity of the therapy and of the virtual environment in accordance with specific patient requirements and abilities. Two MAAT robotic platforms have been developed for the experimental validation of the proposed approach. They consist of the same multimodal interface and differ in the used robotic arm in charge of delivering the therapy. Preliminary experimental data on healthy subjects are reported in this chapter. The application to stroke patients is envisaged.

Keywords

Rehabilitation robotics Human-robot interaction Bio-cooperative control Multi-modal interfaces 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    European Clearing House for Open Robotics Development, EU-funded project within the Seventh Framework Program, GA 231143 (2009-2013), www.echord.info
  2. 2.
    Kempermann, G., Van Praag, H., Gage, F.H.: Activity-dependent regulation of neuronal plasticity and self repair. Prog. Brain Res. 127, 35–48 (2000)CrossRefGoogle Scholar
  3. 3.
    Pellegrino, G., Pellegrino, G., Tombini, M., Assenza, G., Bravi, M., Sterzi, S., Giacobbe, V., Zollo, L., Guglielmelli, E., Cavallo, G., Vernieri, F., Tecchio, F.: Inter-hemispheric coupling changes associate with motor improvements after robotic stroke rehabilitation. Restorative Neurology and Neuroscience 30, 497–510 (2012)Google Scholar
  4. 4.
    Jones, T.A., Chu, C.J., Grande, L.A., Gregory, A.D.: Motor Skills Training Enhances Lesion-Induced Structural Plasticity in the Motor Cortex of Adult Rats. The Journal of Neuroscience 19, 10153–10163 (1999)Google Scholar
  5. 5.
    Butesch, C., Hummelsheim, H., Denzler, P., Mauritz, K.H.: Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand. J. Neurol. Sci. 130, 59–68 (1995)CrossRefGoogle Scholar
  6. 6.
    van der Lee, J.H., Wagenaar, R.C., Lankhorst, G.J., Vogelaar, T.W., Deville, W.L., Bouter, L.M.: Forced use of the upper extremity in chronic stroke patients: Results from a single-blind randomized clinical trial. Stroke 30, 2369–2375 (1999)CrossRefGoogle Scholar
  7. 7.
    Dam, M., Tonin, P., Casson, S., Ermani, M., Pizzolato, G., Iaia, V., Battistin, L.: The effects of long-term rehabilitation therapy on poststroke hemiplegic patients. Stroke 24, 1186–1191 (1993)CrossRefGoogle Scholar
  8. 8.
    Morales, R., Badesa, F.J., Garcia-Aracil, N., Sabater, J.M., Perez-Vidal, C.: Pneumatic robotic systems for upper limb rehabilitation. Medical and Biological Engineering and Computing 49, 1145–1156 (2011)CrossRefGoogle Scholar
  9. 9.
    Wolf, S.L., Binder-Macloud, S.A.: Electromyographic biofeedback applications to the hemiplegic patient: Changes in upper extremity neuromuscular and functibnal status. Journal of the American Physical Therapy Association 63, 1393–1403 (1983)Google Scholar
  10. 10.
    Mudie, M.H., Matyas, T.A.: Can simultaneous bilateral movement involve the undamaged hemisphere in reconstruction of neural networks damaged by stroke? Disabil. Rehabil. 22, 23–37 (2000)CrossRefGoogle Scholar
  11. 11.
    Aisen, M.L., Krebs, H.I., Hogan, N., McDowell, F., Volpe, B.T.: The effect of robot-assisted therapy and rehabilitative training on motor recovery following stroke. Arch. Neurol. 54, 443–446 (1997)CrossRefGoogle Scholar
  12. 12.
    Krebs, H.I., Hogan, N., Aisen, M.L., Volpe, B.T.: Robot-aided Neurorihabilitation. IEEE Trans. Rehabil. Eng. 6, 75–87 (1998)CrossRefGoogle Scholar
  13. 13.
    Krebs, H.I., Volpe, B.T., Ferraro, M., Fasoli, S., Palazzolo, J., Rohrer, B., Edelstein, L., Hogan, N.: Robot-aided neuro-rehabilitation: from evidence-based to science-based rehabilitation. Top Stroke Rehabil. 8, 54–70 (2002)CrossRefGoogle Scholar
  14. 14.
    Volpe, B.T., Krebs, H.I., Hogan, N., Edelstein, L., Diels, C.M., Aisen, M.L.: Robot training enhanced motor outcome in patients with stroke maintained over 3 years. Neurology 53, 1874–1876 (1999)CrossRefGoogle Scholar
  15. 15.
    Burgar, C.G., Lum, P.S., Shor, P.C., Van der Loos, M.: Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience. J. Reh. Res. Dev. 37, 663–673 (2000)Google Scholar
  16. 16.
    Lum, P.S., Burgar, C.G., Shor, P.C., Majmundar, M., Van der Loos, M.: Robot-Assisted Movement Training Compared With Conventional Therapy Techniques for the Rehabilitation of Upper-Limb Motor Function After Stroke. Arch. Phys. Med. Rehabil. 83, 952–959 (2002)CrossRefGoogle Scholar
  17. 17.
    Fasoli, S.D., Krebs, H.I., Stein, J., Frontera, W.R., Hogan, N.: Effects of Robotic Therapy on Motor Impairment and Recovery in Chronic Stroke. Archives of Physical Medicine and Rehabilitation 84, 477–482 (2003)CrossRefGoogle Scholar
  18. 18.
    Nudo, R.J., Friel, K.: Cortical plasticity after stroke: implications for rehabilitation. Rev. Neurol. 155, 713–717 (1999)Google Scholar
  19. 19.
    Staines, W.R., McIlroy, W.E., Graham, S.J., Black, S.E.: Bilateral movement enhances ipsilesional cortical activity in acute stroke: A pilot functional MRI study. Neurology 56, 401–404 (2001)CrossRefGoogle Scholar
  20. 20.
    Prange, G.B., Jannink, M.J.A., Groothuis-Oudshoorn, C.G.M., Hermens, H.J., IJzerman, M.J.: Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. Journal of Rehabilitation Research and Development 43, 171–184 (2006)CrossRefGoogle Scholar
  21. 21.
    Reinkensmeyer, D.J., Kahn, L.E., Averbuch, M., McKenna-Cole, A., Schmit, B.D., Rymer, W.Z.: Understanding and treating arm movement impairment after chronic brain injury:progress with the ARM guide. J. Rehabil. Res. Dev. 37, 653–662 (2000)Google Scholar
  22. 22.
    Kahn, L.E., Zygman, M.L., Rymer, W.Z., Reinkensmeyer, D.: Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: a randomized controlled pilot study. Journal of NeuroEngineering and Rehabilitation 3, 12–23 (2006)CrossRefGoogle Scholar
  23. 23.
    Zollo, L., Accoto, D., Torchiani, F., Formica, D., Guglielmelli, E.: Design of a Planar Robotic Machine for Neuro-rehabilitation. In: International Conference on Robotics and Automation, ICRA 2008, Pasadena, CA (2008)Google Scholar
  24. 24.
    Micera, S., Carrozza, M.C., Guglielmelli, E., Cappiello, G., Zaccone, F., Freschi, C., Colombo, R., Mazzone, A., Delconte, C., Pisano, F., Minuco, G., Dario, P.: A Simple Robotic System for Neurorehabilitation 19, 271–284 (2005)Google Scholar
  25. 25.
    Lum, P.S., Burgar, C.G., Shor, P.C.: Evidence for Improved Muscle Activation Patterns After Retraining of Reaching Movements with the MIME Robotic System in Subjects with Post-Stroke Hemiparesis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 186–194 (2004)Google Scholar
  26. 26.
    Volpe, B.T., Krebs, H.I., Hogan, N., Edelstein, O.L., Diels, C., Aisen, M.: A novel approach to stroke rehabilitation: robotaided sensorimotor stimulation. Neurology 54, 1938–1944 (2000)CrossRefGoogle Scholar
  27. 27.
    Formica, D., Charles, S.K., Zollo, L., Guglielmelli, E., Hogan, N., Krebs, H.I.: The passive stiffness of the wrist and forearm. Journal of Neurophysiology 108, 1158–1166 (2012)CrossRefGoogle Scholar
  28. 28.
    Krebs, H.I., Palazzolo, J.J., Dipietro, L., Ferraro, M., Krol, J., Rannekleiv, K., Volpe, B.T., Hogan, N.: Rehabilitation robotics: performance-based progressive robot-assisted therapy. Autonomous Robots 15, 7–20 (2003)CrossRefGoogle Scholar
  29. 29.
    Formica, D., Zollo, L., Guglielmelli, E.: Torque-dependent compliance control in the joint space for robot-mediated motor therapy. Journal of Dynamic Systems, Measurement, and Control 128, 152–158 (2006)CrossRefGoogle Scholar
  30. 30.
    Patricia, K., Rajibul, H., Jesse, H., Goetschalckx, R., Mihailidis, A.: The development of an adaptive upper-limb stroke rehabilitation robotic system. J. Neuroeng. Rehabil. 8(33), 1–18 (2011)Google Scholar
  31. 31.
    Balasubramanian, S., Zhang, H., Buchanan, S., Austin, H., Herman, R., He, J.: Cooperative and active assistance based interactive therapy. In: 2010 IEEE/ICME International Conference on Complex Medical Engineering (CME), pp. 311–315 (2010)Google Scholar
  32. 32.
    Novak, D., Mihelj, M., Ziherl, J., Olensek, A., Munih, M.: Psychophysiological measurements in a biocooperative feedback loop for upper extremity rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 19, 400–410 (2011)CrossRefGoogle Scholar
  33. 33.
    Koenig, A., Novak, D., Omlin, X., Pulfer, M., Perreault, E., Zimmerli, L., Mihelj, M., Riener, R.: Real-time closed-loop control of cognitive load in neurological patients during robot-assisted gait training. IEEE Transactions on Neural Systems and Rehabilitation Engineering 19, 453–464 (2011)CrossRefGoogle Scholar
  34. 34.
    Duschau-Wicke, A., Caprez, A., Riener, R.: Patient cooperative control increases active participation of individuals with sci during robot-aided gait training. Journal of NeuroEngineering and Rehabilitation 7 (2010)Google Scholar
  35. 35.
    Riener, R., Munih, M.: Guest editorial special section on rehabilitation via bio-cooperative control. IEEE Transactions on Neural Systems and Rehabilitation Engineering 18, 337–338 (2010)CrossRefGoogle Scholar
  36. 36.
    Rodriguez Guerrero, C., Fraile Marinero, J., Perez Turiel, J., Rivera Farina, P.: Bio cooperative robotic platform for motor function recovery of the upper limb after stroke. EMBC Annual International Conference of the IEEE 31, 4472–4475 (2010)Google Scholar
  37. 37.
    Badesa, F.J., Morales, R., Garcia-Aracil, N., Sabater, J.M., Perez-Vidal, C., Fernandez, E.: Multimodal Interfaces to Improve Therapeutic Outcomes in Robot-Assisted Rehabilitation. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42, 1152–1158 (2012)CrossRefGoogle Scholar
  38. 38.
    Zollo, L., Rossini, L., Bravi, M., Magrone, G., Sterzi, S., Guglielmelli, E.: Quantitative evaluation of upper-limb motor control in robot-aided rehabilitation. Medical and Biological Engineering and Computing 9, 1131–1144 (2011)CrossRefGoogle Scholar
  39. 39.
    Zollo, L., Gallotta, E., Guglielmelli, E., Sterzi, S.: Robotic Technologies and Rehabilitation: New Tools for Upper-limb Therapy and Assessment in Chronic Stroke. European Journal of Physical and Rehabilitation Medicine 47, 223–236 (2011)Google Scholar
  40. 40.
    Papaleo, E., Zollo, L., Sterzi, S., Guglielmelli, E.: An inverse kinematics algorithm for upper-limb joint reconstruction during robot-aided motor therapy. In: BIOROB-IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 1983–1988 (2012)Google Scholar
  41. 41.
    Papaleo, E., Zollo, L., Spedaliere, L., Guglielmelli, G.: Patient-Tailored Adaptive Robotic System for Upper-Limb Rehabilitation. In: IEEE International Conference on Robotics and Automation (ICRA 2013), Karlsruhe, Germany, May 6-10 (2013)Google Scholar
  42. 42.
    Rohrer, B., Fasoli, S., Krebs, H.I., Hughes, R., Volpe, B.T., Frontera, W.R., Stein, J., Hogan, N.: Movement smoothness changes during stroke recovery. J. Neurosci. 22, 8297–8304 (2002)Google Scholar
  43. 43.
    Dipietro, L., Krebs, H.I., Fasoli, S.E., Volpe, B.T., Stein, J., Bever, C., Hogan, N.: Changing motor synergies in chronic stroke. J. Neurophysiol. 98, 757–768 (2007)CrossRefGoogle Scholar
  44. 44.
    Marchal-Crespo, L., Reinkensmeyer, D.J.: Review of control strategies for robotic movement training after neurologic injury. Journal of Neuro Engineering and Rehabilitation 6, 20–35 (2009)CrossRefGoogle Scholar
  45. 45.
    Morales, R., Badesa, F.J., Rodriguez, J., Garcia-Aracil, N., Prez, C., Mara-Azorn, J.: A Platform for Researching on Multimodal Robot-Assited Rehabilitation Therapies. In: BIOROB 2012 - IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, Rome, Italy (2012)Google Scholar
  46. 46.
    Novak, D., Ziherl, J., Olensek, A., Milavec, M., Podobnik, J., Mihelj, M., Munih, M.: Psychophysiological responses to robotic rehabilitation tasks in stroke. IEEE Transactions on Neural Systems and Rehabilitation Engineering 18, 351–361 (2010)CrossRefGoogle Scholar
  47. 47.
    Bradley, M.M., Lang, P.: Measuring emotion: the self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry 25, 49–59 (1994)CrossRefGoogle Scholar
  48. 48.
    Krebs, H.I., Hogan, N., Volpe, B.T., Aisen, M.L., Edelstein, L., Diel, C.: Overview of clinical trials with MIT-MANUS: a robot-aided neuro-rehabilitation facility. Technology and Health Care 7, 419–423 (1999)Google Scholar
  49. 49.
    Levin, M.F., Selles, R.W., Verheul, M.H., Meijer, O.G.: Deficits in the coordination of agonist and antagonist muscles in stroke patients: implications for normal motor control. Brain Res. 853, 352–369 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Loredana Zollo
    • 1
    Email author
  • Eugenia Papaleo
    • 1
  • Luca Spedaliere
    • 1
  • Eugenio Guglielmelli
    • 1
  • Francisco Javier Badesa
    • 2
  • Ricardo Morales
    • 2
  • Nicolas Garcia-Aracil
    • 2
  1. 1.Laboratory of Biomedical Robotics and BiomicrosistemsUniversitá Campus Bio-Medico di RomaRomeItaly
  2. 2.Virtual Reality and Robotics LaboratoryUniversidad Miguel Hernández de ElcheElcheSpain

Personalised recommendations