Autonomous Robots

, Volume 15, Issue 1, pp 7–20 | Cite as

Rehabilitation Robotics: Performance-Based Progressive Robot-Assisted Therapy

  • H.I. Krebs
  • J.J. Palazzolo
  • L. Dipietro
  • M. Ferraro
  • J. Krol
  • K. Rannekleiv
  • B.T. Volpe
  • N. Hogan
Article

Abstract

In this paper we describe the novel concept of performance-based progressive robot therapy that uses speed, time, or EMG thresholds to initiate robot assistance. We pioneered the clinical application of robot-assisted therapy focusing on stroke—the largest cause of disability in the US. We have completed several clinical studies involving well over 200 stroke patients. Research to date has shown that repetitive task-specific, goal-directed, robot-assisted therapy is effective in reducing motor impairments in the affected arm after stroke. One research goal is to determine the optimal therapy tailored to each stroke patient that will maximize his/her recovery. A proposed method to achieve this goal is a novel performance-based impedance control algorithm, which is triggered via speed, time, or EMG. While it is too early to determine the effectiveness of the algorithm, therapists have already noted one very strong benefit, a significant reduction in arm tone.

rehabilitation robotics stroke robot-aided neurorehabilitation adaptive algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aisen, M.L., Krebs, H.I., McDowell, F., Hogan, N., and Volpe, B.T. 1997. The effect of robot assisted therapy & rehabilitative training on motor recovery following a stroke. Archives of Neurology, 54:443–446.Google Scholar
  2. Aizawa, H., Inase, M., Mushiake, H., Shima, K., and Tanji, J. 1994. Reorganization of supplementary motor areas after neocortical damage. Exp. Br. Res., 84:668–671.Google Scholar
  3. Akazawa, K., Milner, T.E., and Stein, R.B. 1983. Modulation of reflex EMG and stiffness in response to stretch of human finger muscle. J. of Neurophysiology, 49:16–27.Google Scholar
  4. Asanuma, C. 1991. Mapping movements within a moving motor map. Trends in Neuroscience, 14:217–218.Google Scholar
  5. Brainin, M., Bornstein, N., Boysen, G., and Demarin, V. 2000. Acute neurological stroke care in Europe: Results of the European stroke care inventory. European Journal of Neurology, 7:5–10.Google Scholar
  6. Buerger, S.P., Krebs, H.I., and Hogan, N. 2001. Characterization and control of a screw-driven robot for neurorehabilitation. IEEE—CCA/ISIC 2001.Google Scholar
  7. Celestino, J., Krebs, H.I., and Hogan, N. in press. A robot for wrist rehabilitation: Characterization and initial results. ICORR-2003.Google Scholar
  8. Classen, J., Liepert, J., Wise, S.P., Hallet, M., and Cohen, L.G. 1998. Rapid plasticity of human cortical representation induced by practice. Journal of Neurophysiology, 79(2):1117–1123.Google Scholar
  9. Cozens, J.A. 1999. Robotic assistance of an active upper limb exercise in neurologically impaired patients. IEEE Transactions on Rehabilitation Engineering, 7(2).Google Scholar
  10. Dewald, J.P.A., Pope, P.S., Given, J., Buchanan, T.S., and Rymer, W.Z. 1995. Abnormal muscle coactivation patterns during isometric torque generation at the elbow and shoulder in hemiparetic subjects. Brain, 118(2):495–510.Google Scholar
  11. Diamond, M.C., Greer, E.R., York, A., Lewis, D., Barton, T., and Lin, J. 1987. Rat cortical morphology following crowded enriched living conditions. Exp. Neurol, 87(2):309–317.Google Scholar
  12. Diamond, M.C., Ingham, C.A., Johnson, R.E., Bennet, E.L., and Rozenzweig, M.R. 1976. Effects of environment on morphology of rat cerebral cortex and hippocampus. J. Neurobiol, 7(1):75–85.PubMedGoogle Scholar
  13. Diamond, M.C., Johnson, R.E., Protti, A.M., Ott, C., and Kajisa, L. 1985. Plasticity in the 904-day-old male rat cerebral cortex. Exp. Neurol, 87(2):309–317.Google Scholar
  14. Diamond, M.C., Law, F., Rhodes, H., Lindner, B., Rosenzweig, M.R., Krech, D., and Bennet, E.L. 1966. Increases in cortical depth and glia numbers in rats subjected to enriched environment. J. Comp Neurol, 128(1):117–126.Google Scholar
  15. Ferraro, M., Demaio, J.H., Krol, J., Trudell, C., Edelstein, L., Christos, P., England, J., Fasoli, S., Aisen, M.L., Krebs, H.I., Hogan, N., and Volpe, B.T. 2002. Assessing the motor status score: Ascale for the evaluation of upper limb motor outcomes in patients after stroke. Neurorehabil Neural Repair, 16(3):301–307.Google Scholar
  16. Fasoli, S.E., Krebs, H.I., Stein, J., Frontera, W.R., and Hogan, N. 2003. Effects of robotic therapy on motor impairment and recovery in chronic stroke. Arch. Physical Medicine, 84:477–482.Google Scholar
  17. Fisher, C.M. 1992. Concerning the mechanism of recovery in stroke hemiplegia. Can. J. Neurol. Sci., 19:57–63.Google Scholar
  18. Fukuda, O., Tsuji, T., and Kaneko, M. 1997. An EMG controlled manipulator using ANNs. IEEE InternationalWorkshop on Robot and Human Communication.Google Scholar
  19. Fukuda, O., Tsuji, T., Ohtsuka, A., and Kaneko, M. 1998. EMG-based human-robot interface for rehabilitation aid. In Proc. 1998 IEEE International Conference on Robotics and Automation, Leuven, Belgium.Google Scholar
  20. Fukuda, O., Tsuji, T., Kane KO, M., Otsuka, A. 2003. A human-assisting manipulator teleperated by EMG signals and arm motions. IEEE Robotics and Automation, 19(2):210–222.Google Scholar
  21. Glees, P. 1980. Functional reorganization following hemispherectomy in man and after small experimental lesions in primates. In Recovery of Function: Theoretical Considerations for Brain Injury Rehabilitation, Bach-y-Rita P. (Ed.), Baltimore, University Park Press.Google Scholar
  22. Graupe, D., Kohn, K.H., Kralj, A., and Basseas, S. 1983. Patient controlled electrical stimulation via EMG signature discrimination for providing certain paraplegics with primitive walking functions. J. Biomed. Eng., 5:220–226.Google Scholar
  23. Greer, E.R., Diamond, M.C., and Murphy, G.M. Jr. 1982. Increased branching of basal dendrites on pyramidal neurons in the occipital cortex of homozygous Brattleboro rats in standard and enriched environmental conditions: A Golgi study. Exp Neurol, 76(2):254–262.Google Scholar
  24. Gresham, G.E., Duncan, P.W., Stason W.B. et al. 1995. Post-Stroke Rehabilitation. Clinical Practice Guideline, No. 16. U.S. Dept. Health and Human Services. Public Health Service, Agency for Health Care Policy and Research. AHCPR Publication No. 95-0662.Google Scholar
  25. Hogan, N., Krebs, H.I., Sharon, A., and Charnnarong, J. 1995. Interactive robotic therapist. US Patent No. 5,466,213.Google Scholar
  26. Hogan, N. 1976. A review of the methods of processing EMG for use as a proportional control signal. Biomed. Eng., 11:81–86.Google Scholar
  27. Hogan, N. 1985. Impedance control: An approach to manipulation. ASME-Journal Dyn Syst Measure Control, 107:1–24.Google Scholar
  28. Jacobs, K.M. and Konoghue, J.P. 1991. Reshaping the cortical motor map by unmasking latent intracortical connections. Science, 251:944–947.Google Scholar
  29. Jenkins, W.M. and Merzenich, M.M. 1987. Reorganization of neocortical representations after brain injury. Progress in Brain Research, 71:249–266.Google Scholar
  30. Jones, T.A. and Shallert, T. 1994. Used dependent growth after neocortical damage. J. Neuroscience, 14:2140–2152.Google Scholar
  31. Jugenheimer, K.A., Hogan, N., and Krebs, H.I. 2001. A robot for hand rehabilitation: A continuation of the MIT-MANUS neurorehabilitation workstation. ASME 2001 IDETC/CIE.Google Scholar
  32. Kaas, J.H., Krubitzer, L.A., Chino, Y.M. et al. 1990. Reorganization of retinotopic cortical maps in adult mammals after lesions of the retina. Science, 248:229–231.Google Scholar
  33. Kato, I., Okazaki, E., Kikuchi, H., and Iwanami, K. 1967. Electropneumatically controlled hand prosthesis using pattern recognition of mio-electric signals. In Proc. 7th ICMBE.Google Scholar
  34. Kazerooni, H. 1990. Human-robot interaction via the transfer of power and information signals. IEEE Transactions on Systems, Man, and Cybernetics, 20(2):450–463.Google Scholar
  35. Kearney, R.E. and Mirbagheri, M.M. 2001. Identification and simulation as tools for measurement of neuromuscular properties. IEEE—23rd EMBS.Google Scholar
  36. King, A.J. and Moore, D.R. 1991. Plasticity of auditory maps in the brain. Trends in Neuroscience, 14:21–27.Google Scholar
  37. Krebs, H.I., Hogan, N., Aisen, M.L., and Volpe, B.T. 1998. Robotaided neuro-rehabilitation. IEEE-Transactions on Rehabilitation Engineering, 6(1):75–87.Google Scholar
  38. Krebs, H.I., Hogan, N., Aisen, M.L., and Volpe, B.T. 1999. Quantization of continuous arm movements in humans with brain injury. Proc. National. Academy of Science, 96:4645–4649.Google Scholar
  39. Krebs, H.I., Hogan, N., Hening, W., Adamovich, S., and Poizner, H. 2001. Procedural motor learning in parkinson's disease. Exp. Brain Res., 141:425–437.Google Scholar
  40. Krebs, H.I., Volpe, B.T., Aisen, M.L., and Hogan, N. 2000. Increasing productivity and quality of care: Robot-aided neurorehabilitation. VA Journal of Rehabilitation Research and Development, 37(6):639–652.Google Scholar
  41. Krebs, H.I., Volpe, B.T., Ferraro, M., Fasoli, S., Palazzolo, J., Rohrer, B., Edelstein, L., and Hogan, N. 2002. Robot-aided neurorehabilitation: From evidence-based to science-based rehabilitation. Topics in Stroke Rehabilitation, 8(4):54–70.Google Scholar
  42. Krebs, H.I., Volpe, B.T., Palazzolo, J.J., Fasoli, S.E., Ferraro, M., Edelstein, L., and Hogan, N. 2001. Disturbances of higher level neural control—Robotic applications in stroke. IEEE—23rd EMBS, Istanbul, Turkey.Google Scholar
  43. Krebs, H.I., Volpe, B.T., Palazzolo, J., Rohrer, B., Ferraro, M., Fasoli, S., Edelstein, L., and Hogan, N. 2001. Robot-aided neuro-rehabilitation in stroke: Interim results on the follow-up of 76 patients and on movement performance indices. In Integration of Assistive Technology in the Information Age, Mounir Mokhtari (Ed.), IOS Press, Assistive Technology Research Series, Amsterdam, 2001.Google Scholar
  44. Lum, P.S., Burgar, C.G., Shor, P., Majmundar, M., and Van der Loos, M. 2000. Robot-assisted movement training compared with convention therapy techniques for the rehabilitation of upperpinto motor junc after stroke arch. Phys. Med. Rehab., 83:952–959.Google Scholar
  45. Medical Research Council/Guarantors of Brain. 1986. Aids to the examination of the peripheral nervous system. London, Bailliere Tindall.Google Scholar
  46. Merzenich, M.M., Jenkins, W.M., Johnston, P., Schreiner, C., Miller, S.L., and Tallal, P. 1996. Temporal processing deficits of language-learning impaired children ameliorated by training. Science, 271:77–81.Google Scholar
  47. Merzenich, M.M., Nelson, R.J., Stryker, M.P., Cynader, M.S., Schoppmann, A., and Zook, J.M. 1984. Somatosensory cortical map changes following digit amputation in adult monkeys. J Comparative Neurology, 224:591–605.Google Scholar
  48. Mirbagheri, M.M., Barbeau, H., Ladouceur, M., and Kearney, R.E. 2001. Intrinsic and reflex stiffness in normal and spastic, spinal cord injured subjects. Exp Brain Res, 141:446–459.Google Scholar
  49. Mussa-Ivaldi, F.A., Hogan, N., and Bizzi, E. 1985. Neural, mechanical, and geometric factors subserving arm posture in humans. The Journal of Neuroscience, 5(10):2732–2743.Google Scholar
  50. Nudo, R.J., Wise, B.M., SiFuentes, F., and Milliken, G.W. 1996. Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science, 272:1791–1794.PubMedGoogle Scholar
  51. Nyberg-Hansen, R. and Rinvik, E. 1963. Some comments on the pyramidal tract, with special reference to its individual variations in man. Acta Neurol. Scand., 39:1–30.Google Scholar
  52. Patton, J. and Mussa-Ivaldi, F.A. 2001. Robotic teaching by exploiting the nervous system's adaptive mechanisms. In Integration of Assistive Technology in the Information Age, Mounir Mokhtari (Ed.), IOS Press, Assistive Technology Research Series, Amsterdam, 2001.Google Scholar
  53. Peckham, P.H., Marsolais, E.B., and Mortimer, J.T. 1980. Restoration of key grip and release in C6 tetraplegic patient through functional electrical stimulation. J. Hand Surg., 5(5):462–469.Google Scholar
  54. Philipson, L. 1985. Adaptable myoelectric prosthetic control with functional visual feedback using microprocessor techniques. Med. Biol. Eng. Comput., 23:8–14.Google Scholar
  55. Pons, T.P., Garraghty, P.E., and Mishkin, M. 1988. Lesion-induced plasticity in the second somatosensory cortex of adult macaques. Proc. Natl. Acad. Sci., 85:5279–5281.Google Scholar
  56. Reinkensmeyer, D., Schmit, B.D., and Rymer, Z. 1999. Assessment of active and passive restraint during guided reaching after chronic brain injury. Annals of Biomedical Engineering, 27:805–814.Google Scholar
  57. Ridding, M.C., Sheean, G., Rothwell, J.C., Inzelberg, R., and Kujirai, T. 1995. Changes in the balance between motor cortical excitation and inhibition in focal, task specific dystonia. J Neurol Neurosurg Psychiatry, 59:493–498.Google Scholar
  58. Rosen, J., Brand, M., Fuchs, M.B., and Arcan, M. 2001. A myosignal based powered exoskeleton system. IEEE Transaction on Systems, Man, and Cybernetics, 31(3).Google Scholar
  59. Sabatini, U., Toni, D., Pantano, P. et al. 1994. Motor recovery after early brain damage. Stroke, 25:514–517.Google Scholar
  60. Stefan, K., Kunesch, E., Cohen, L.G., Benecke, R., and Classen, J. 2000. Induction of plasticity in the human motor cortex by paired associative stimulation. Brain, 123:572–584.Google Scholar
  61. Triolo, R.J. and Moscowitz, G.D. 1985. A multi-channel time series myoprocessor for robust classification of limb functiona and estimation of muscle force. In IEEE Proc. 7th Annual Conf. Eng. Med. Biol. Soc..Google Scholar
  62. Volpe, B.T., Krebs, H.I., Hogan, N., Edelstein, L., Diels, C.M., and Aisen, M.L. 1999. Robot training enhanced motor outcome in patients with stroke maintained over 3 years. Neurology, 53:1874–1876.Google Scholar
  63. Volpe, B.T., Krebs, H.I., Hogan, N., Edelstein, L., Diels, C.M., and Aisen, M. 2000. A novel approach to stroke rehabilitation: Robot aided sensorymotor stimulation. Neurology, 54:1938–1944.Google Scholar
  64. Volpe, B.T., Krebs, H.I., and Hogan, N. 2001. Is robot-aided sensorimotor training in stroke rehabilitation a realistic option? Current Opinion in Neurology, Lippincott Williams & Wilkins.Google Scholar
  65. Williams, D.J., Krebs, H.I., Hogan, N. 2001. A Robot for wrist rehabilitation. IEEE-23rd EMBS.Google Scholar
  66. Wu, C.-Y., Trombly, C.A., Lin, K.-C., and Tickle-Degnen, L. 1998. Effects of object affordances on movement performance: A metaanalysis. Scandinavian J. of Occupational Therapy, 5:83–92.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • H.I. Krebs
    • 1
    • 2
  • J.J. Palazzolo
    • 1
  • L. Dipietro
    • 3
  • M. Ferraro
    • 4
  • J. Krol
    • 4
  • K. Rannekleiv
    • 4
  • B.T. Volpe
    • 2
  • N. Hogan
    • 1
    • 5
  1. 1.Department of Mechanical Engineering, Newman Laboratory for Biomechanics and Human RehabilitationMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department Neurology and Neuroscience, Burke Medical Research InstituteWeill Medical College of Cornell UniversityWhite PlainsUSA
  3. 3.Scuola Superiore Sant'AnnaPisaItaly
  4. 4.Burke Rehabilitation HospitalWhite PlainsUSA
  5. 5.Department of Brain and Cognitive SciencesMassachusetts Institute of TechnologyUSA

Personalised recommendations