Quantitative evaluation of upper-limb motor control in robot-aided rehabilitation

  • Loredana Zollo
  • Luca Rossini
  • Marco Bravi
  • Giovanni Magrone
  • Silvia Sterzi
  • Eugenio Guglielmelli
Special Issue - Original Article

Abstract

This paper is focused on the multimodal analysis of patient performance, carried out by means of robotic technology and wearable sensors, and aims at providing quantitative measure of biomechanical and motion planning features of arm motor control following rehabilitation. Upper-limb robotic therapy was administered to 24 community-dwelling persons with chronic stroke. Performance indices on patient motor performance were computed from data recorded with the InMotion2 robotic machine and a magneto-inertial sensor. Motor planning issues were investigated by means of techniques of motion decomposition into submovements. A linear regression analysis was carried out to study correlation with clinical scales. Robotic outcome measures showed a significant improvement of kinematic motor performance; improvement of dynamic components was more significant in resistive motion and highly correlated with MP. The analysis of motion decomposition into submovements showed an important change with recovery of submovement number, amplitude and order, tending to patterns measured in healthy subjects. Preliminary results showed that arm biomechanical functions can be objectively measured by means of the proposed set of performance indices. Correlation with MP is high, while correlation with FM is moderate. Features related to motion planning strategies can be extracted from submovement analysis.

Keywords

Robot-aided rehabilitation Motor assessment 

References

  1. 1.
    Berthier NE (1996) Learning to reach: a mathematical model. Dev Psychol 32:811–823CrossRefGoogle Scholar
  2. 2.
    Bhushan N, Shadmehr R (1999) Computational nature of human adaptive control during learning of reaching movements in force fields. Biol Cybern 81:39–60PubMedCrossRefGoogle Scholar
  3. 3.
    Bosecker C, Dipietro L, Volpe B, Krebs HI (2010) Kinematic robot-based evaluation scales and clinical counterparts to measure upper limb motor performance in patients with chronic stroke. Neurorehabil Neural Repair 24:1545–9683Google Scholar
  4. 4.
    Bowen A, Lincoln NB (2007) Cognitive rehabilitation for spatial neglect following stroke. Cochrane Database Syst Rev (2)Google Scholar
  5. 5.
    Brewer BR, McDowell SK, Worthen-Chaudhari LC (2007) Poststroke upper extremity rehabilitation: a review of robotic systems and clinical results. Top Stroke Rehabil 14:22–44PubMedCrossRefGoogle Scholar
  6. 6.
    Cirstea MC, Levin MF (2000) Compensatory strategies for reaching in stroke. Brain 123:940–953PubMedCrossRefGoogle Scholar
  7. 7.
    Colombo R, Pisano F, Micera S, Mazzone A, Delconte C, Carrozza MC, Dario P, Minuco G (2008) Assessing mechanisms of recovery during robot-aided neurorehabilitation of the upper limb. Neurorehabil Neural Repair 22:50–63PubMedGoogle Scholar
  8. 8.
    Cramer SC (2010) Brain repair after stroke. N Engl J Med 362:1827–1829PubMedCrossRefGoogle Scholar
  9. 9.
    Duncan PW, Propst M, Nelson SG (1983) Reliability of the Fugl-Meyer assessment of sensorimotor recovery following cerebrovascular accident. Phys Ther 63:1606–1610PubMedGoogle Scholar
  10. 10.
    Fishbach A, Roy SA, Bastianen C, Miller LE, Houk JC (2005) Kinematic properties of on-line error corrections in the monkey. Exp Brain Res 164:442–457PubMedCrossRefGoogle Scholar
  11. 11.
    Fishbach A, Roy SA, Bastianen C, Miller LE, Houk JC (2007) Deciding when and how to correct a movement: discrete submovements as a decision making process. Exp Brain Res 177:45–63PubMedCrossRefGoogle Scholar
  12. 12.
    Flash T, Henis E (1991) Arm trajectory modifications during reaching towards visual targets. J Cogn Neurosci 3:220–230CrossRefGoogle Scholar
  13. 13.
    Gomez-Pinilla F, Ying Z, Roy RR, Molteni R, Edgerton VR (2002) Voluntary exercise induces a BDNF-mediated mechanism that promotes neuroplasticity. J Neurophysiol 88:2187–2195PubMedCrossRefGoogle Scholar
  14. 14.
    Gregson JM, Leathley MJ, Moore AP, Smith TL, Sharma AK, Watkins CL (2000) Reliability of measurements of muscle tone and muscle power in stroke patients. Age Ageing 29:223–228PubMedCrossRefGoogle Scholar
  15. 15.
    Guglielmelli E, Johnson MJ, Shibata T (2009) Guest editorial special issue on rehabilitation robotics. IEEE Trans Robot 25:477–480CrossRefGoogle Scholar
  16. 16.
    Hoffmann T, Bennett S, Koh CL, McKenna K (2010) A systematic review of cognitive interventions to improve functional ability in people who have cognitive impairment following stroke. Top Stroke Rehabil 17(2):99–107PubMedCrossRefGoogle Scholar
  17. 17.
    Hogan N, Sternad D (2009) Sensitivity of smoothness measures to movement duration, amplitude, and arrests. J Mot Behav 41:6CrossRefGoogle Scholar
  18. 18.
    Hogan N, Krebs HI, Sharon A, Charnnarong J (1995) Interactive robotic therapist. Massachusetts Institute of Technology, Cambridge, U.S. Patent #5466213Google Scholar
  19. 19.
    Jackson SL (2003) Research methods, statistics: a critical thinking approach. Wadsworth/Thomson Learning Ed, BelmontGoogle Scholar
  20. 20.
    Jones TA, Chu CJ, Grande LA, Gregory AD (1999) Motor skills training enhances lesion-induced structural plasticity in the motor cortex of adult rats. J Neurosci 19:10153–10163PubMedGoogle Scholar
  21. 21.
    Kawato M (1992) Optimization and learning in neural networks for formation and control of coordinated movement. In: Meyer DE, Kornblum S (eds) Attention and performance, vol XIV. MIT Press, Cambridge, pp 821–849Google Scholar
  22. 22.
    Kempermann G, Van Praag H, Gage FH (2000) Activity-dependent regulation of neuronal plasticity and self repair. Prog Brain Res 127:35–48PubMedCrossRefGoogle Scholar
  23. 23.
    Krakauer JW (2006) Motor learning: its relevance to stroke recovery and neurorehabilitation. Curr Opin Neurol 19:84–90PubMedCrossRefGoogle Scholar
  24. 24.
    Krebs HI, Hogan N, Aisen ML, Volpe BT (1998) Robot-aided neurorehabilitation. IEEE Trans Rehabil Eng 6:75–87PubMedCrossRefGoogle Scholar
  25. 25.
    Krebs HI, Palazzolo JJ, Dipietro L, Ferraro M, Krol J, Rannekleiv K, Volpe BT, Hogan N (2003) Rehabilitation robotics: performance-based progressive robot-assisted therapy. Auton Robots 15:7–20CrossRefGoogle Scholar
  26. 26.
    Krebs HI, Volpe BT, Williams D, Celestino J, Charles SK, Lynch D, Hogan N (2007) Robot-aided neurorehabilitation: a robot for wrist rehabilitation. IEEE Trans Neural Syst Rehabil Eng 15:327–335PubMedCrossRefGoogle Scholar
  27. 27.
    Kwakkel G, Kollen B, Lindeman E (2004) Understanding the pattern of functional recovery after stroke: facts and theories. Restor Neurol Neurosci 22:281–299PubMedGoogle Scholar
  28. 28.
    Kwakkel G, van Peppen R, Wagenaar RC, Wood Dauphinee S, Richards C, Ashburn A, Miller K, Lincoln N, Partridge C, Wellwood I, Langhorne P (2004) Effects of augmented exercise therapy time after stroke: a meta-analysis. Stroke 35:2529–2536PubMedCrossRefGoogle Scholar
  29. 29.
    Latash ML, Anson JG (1996) What are “normal” movements in atypical populations? Behav Brain Sci 19:55–106CrossRefGoogle Scholar
  30. 30.
    Lee D, Port NL, Georgopoulos AP (1997) Manual interception of moving targets II. On-line control of overlapping submovements. Exp Brain Res 116:421–433PubMedCrossRefGoogle Scholar
  31. 31.
    Levin MF, Kleim JA, Wolf SL (2009) What do motor “Recovery” and “Compensation” mean in patients following stroke? Neurorehabil Neural Repair 23:313–319PubMedGoogle Scholar
  32. 32.
    Lo AC, Guarino PD, Richards LG, Haselkorn JK, Wittenberg GF, Federman DG, Ringer RJ, Wagner TH, Krebs HI, Volpe BT, Bever CT, Bravata DM, Duncan PW, Corn BH, Maffucci AD, Nadeau SE, Conroy SS, Powell JM, Huang GD, Peduzzi P (2010) Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med 362:1772–1783PubMedCrossRefGoogle Scholar
  33. 33.
    Masur H (2008) The rational use of robots in neurorehabilitation-fact or fiction? Dtsch Arztebl Int 105:329PubMedGoogle Scholar
  34. 34.
    Medical Research Council/Guarantors of Brain (1986) Aids to the examination of the peripheral nervous system. Bailliere Tindall, LondonGoogle Scholar
  35. 35.
    Mehrholz J, Platz T, Kugler J, Pohl M (2009) Electromechanical and robot-assisted arm training for improving arm function and activities of daily living after stroke (Review). Cochrane Lib 4:1–44Google Scholar
  36. 36.
    Meyer DE, Abrams RA, Kornblum S, Wright CE, Smith JE (1988) Optimality in human motor performance: ideal control of rapid aimed movements. Psychol Rev 95:340–370PubMedCrossRefGoogle Scholar
  37. 37.
    Milner T (1992) A model for the generation of movements requiring endpoint precision. Neuroscience 49:487–496PubMedCrossRefGoogle Scholar
  38. 38.
    Morasso P (1981) Spatial control of arm movements. Exp Brain Res 42:223–227PubMedCrossRefGoogle Scholar
  39. 39.
    Novak KE, Miller LE, Houk JC (2000) Kinematics of rapid hand movements in a knobturning task. Exp Brain Res 132:419–433PubMedCrossRefGoogle Scholar
  40. 40.
    Novak KE, Miller LE, Houk JC (2002) The use of overlapping submovements in the control of rapid hand movements. Exp Brain Res 144(3):351–364PubMedCrossRefGoogle Scholar
  41. 41.
    Nudo RJ, Friel KM (1999) Cortical plasticity after stroke: implications for rehabilitation. Rev Neurol 155:713–717PubMedGoogle Scholar
  42. 42.
    Prange GB, Jannink MJA, Groothuis-Oudshoorn CGM, Hermens HJ, IJzerman MJ (2006) Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. J Rehabil Res Dev 43:171–184PubMedCrossRefGoogle Scholar
  43. 43.
    Rohrer B, Hogan N (2003) Avoiding spurious submovement decompositions: a globally optimal algorithm. Biol Cybern 89:190–199PubMedCrossRefGoogle Scholar
  44. 44.
    Rohrer B, Hogan N (2006) Avoiding spurious submovement decompositions II: a scattershot algorithm. Biol Cybern 94:409–414PubMedCrossRefGoogle Scholar
  45. 45.
    Rohrer B, Fasoli S, Krebs HI et al (2002) Movement smoothness changes during stroke recovery. J Neurosci 22:8297–8304PubMedGoogle Scholar
  46. 46.
    Rossini L (2010) Neuroinspired interfaces for human-machine interaction. PhD ThesisGoogle Scholar
  47. 47.
    Saunders JA, Knill DC (2003) Humans use continuous visual feedback from the hand to control fast reaching movements. Exp Brain Res 152:341–352PubMedCrossRefGoogle Scholar
  48. 48.
    Shadmehr R, Mussa-Ivaldi FA (1994) Adaptive representation of dynamics during learning of a motor task. J Neurosci 14:3208–3224PubMedGoogle Scholar
  49. 49.
    Smith MA, Shadmehr R (2005) Intact ability to learn internal models of arm dynamics in Huntington’s disease but not cerebellar degeneration. J Neurophysiol 93:2809–2821PubMedCrossRefGoogle Scholar
  50. 50.
    Smith MA, Brandt J, Shadmehr R (2000) Motor disorder in Huntington’s disease begins as a dysfunction in error feedback control. Nature 403:544–549PubMedCrossRefGoogle Scholar
  51. 51.
    Steenbergen B, Van Thiel E, Hulstijn W, Meulenbroek RGJ (2000) The coordination of reaching and grasping in spastic hemiparesis. Hum Mov Sci 19:75–105CrossRefGoogle Scholar
  52. 52.
    Takahashi CD, Der-Yeghiaian L, Le V, Motiwala RR, Cramer SC (2008) Robot-based hand motor therapy after stroke. Brain 131:425–437PubMedCrossRefGoogle Scholar
  53. 53.
    Timmermans AAA, Seelen HAM, Willmann RD, Kingma H (2009) Technology-assisted training of arm-hand skills in stroke: concepts on reacquisition of motor control and therapist guidelines for rehabilitation technology design. J Neuroeng Rehabil 6:1–18PubMedCrossRefGoogle Scholar
  54. 54.
    Vallbo AB, Wessberg J (1993) Organization of motor output in slow finger movements in man. J Physiol 469:673–691PubMedGoogle Scholar
  55. 55.
    Woodworth RS (1899) The accuracy of voluntary movement. J Nerv Ment Dis 26:743–752CrossRefGoogle Scholar
  56. 56.
    Zollo L, Salerno A, Rossini L, Guglielmelli E (2010) Submovement composition for motion and interaction control of a robot manipulator. In: Proceedings of the IEEE international conference on biomedical robotics and biomechatronics. Tokyo, JapanGoogle Scholar
  57. 57.
    Zollo L, Gallotta E, Guglielmelli E, Sterzi S (2011) Robotic technologies and rehabilitation: new tools for upper-limb therapy and assessment in chronic stroke. Eur J Phys Rehabil Med 47:223–236Google Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2011

Authors and Affiliations

  • Loredana Zollo
    • 1
  • Luca Rossini
    • 2
  • Marco Bravi
    • 3
  • Giovanni Magrone
    • 3
  • Silvia Sterzi
    • 3
  • Eugenio Guglielmelli
    • 1
  1. 1.Laboratory of Biomedical Robotics and BiomicrosystemsUniversità Campus Bio-MedicoRomeItaly
  2. 2.IRCCS San Raffaele PisanaRomeItaly
  3. 3.Clinic of Physical Medicine and RehabilitationUniversità Campus Bio-MedicoRomeItaly

Personalised recommendations