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Assessing the effectiveness of robot facilitated neurorehabilitation for relearning motor skills following a stroke

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Abstract

A growing awareness of the potential for machine-mediated neurorehabilitation has led to several novel concepts for delivering these therapies. To get from laboratory demonstrators and prototypes to the point where the concepts can be used by clinicians in practice still requires significant additional effort, not least in the requirement to assess and measure the impact of any proposed solution. To be widely accepted a study is required to use validated clinical measures but these tend to be subjective, costly to administer and may be insensitive to the effect of the treatment. Although this situation will not change, there is good reason to consider both clinical and mechanical assessments of recovery. This article outlines the problems in measuring the impact of an intervention and explores the concept of providing more mechanical assessment techniques and ultimately the possibility of combining the assessment process with aspects of the intervention.

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Notes

  1. Hocoma, Switzerland.

  2. A Hammerstein model is a simple non-linear model that consists of a static non-linear element that shapes the input variable, followed by a linear dynamic element.

References

  1. Amirabdollahian F, Loureiro R, Harwin W (2002) Minimum jerk trajectory control for rehabilitation and haptic applications. In: Proceedings of the 2002 IEEE international conference on robotics and automation, Washington, DC, pp 3380–3385

  2. Bennett D, Hollerbach J, Xu Y, Hunter I (1992) Time-varying stiffness of human elbow joint during cyclic voluntary movement. Exp Brain Res 88:433–442. doi:10.1007/BF02259118

    Article  PubMed  CAS  Google Scholar 

  3. Blakemore SJ, Frith CD, Wolpert DM (2001) The cerebellum is involved in predicting the sensory consequences of action. Neuroreport 12(9):1879–1884

    Article  PubMed  CAS  Google Scholar 

  4. Boyke J, Driemeyer J, Gaser C, Buchel C, May A (2008) Training-induced brain structure changes in the elderly. J Neurosci 28(28):7031

    Article  PubMed  CAS  Google Scholar 

  5. Burdet E, Osu R, Franklin DW, Milner TE, Kawato M (2001) The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature 414: 446–449

    Google Scholar 

  6. Cramer S (2010) Brain repair after stroke. N Engl J Med 362(19):1827–1829

    Article  PubMed  CAS  Google Scholar 

  7. Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A (2004) Neuroplasticity: changes in grey matter induced by training. Nature 427:311–312. doi:10.1038/427311a

    Article  PubMed  CAS  Google Scholar 

  8. Engvig A, Fjell A, Westlye L, Moberget T, Sundseth Ř, Larsen V, Walhovd K (2010) Effects of memory training on cortical thickness in the elderly. NeuroImage 54(4): 1667–1676

    Article  Google Scholar 

  9. Galvin R, Cusack T, Stokes E (2008) A randomised controlled trial evaluating family mediated exercise (FAME) therapy following stroke. BMC Neurol 8(1):22

    Article  PubMed  Google Scholar 

  10. Galvin R, Murphy B, Cusack T, Stokes E (2008) The impact of increased duration of exercise therapy on functional recovery following stroke—what is the evidence? Top Stroke Rehabil 15(4):365–377. doi:10.1310/tsr1504-365

    Article  PubMed  Google Scholar 

  11. Galvin R, Cusack T, O’Grady E, Murphy B, Stokes E (2011) Family mediated exercise intervention [fame]: evaluation of a novel form of exercise delivery after stroke. Stroke 42:681–686

    Article  Google Scholar 

  12. Gassert R, Moser R, Burdet E, Bleuler H (2006) MRI/fMRI-compatible robotic system with force feedback for interaction with human motion. IEEE/ASME Trans Mechatron 11(2):216–224

    Article  Google Scholar 

  13. Given J, Dewald J, Rymer W (1995) Joint dependent passive striffness in paretic and contralateral limbs of spastic patients with hemiparetic stroke. J Neurol Neurosurg Psychiatry 59:271–279

    Article  PubMed  CAS  Google Scholar 

  14. Harwin W, Patton J, Edgerton V (2006) Challenges and opportunities for robot mediated neurorehabilitation. Proc IEEE 94(9):1717–1726

    Article  Google Scholar 

  15. Hribar A, Munih M (2010) Development and testing of fMRI-compatible haptic interface. Robotica 28(02):259–265

    Article  Google Scholar 

  16. International classification of functioning, disability and health. WHO, Geneva, Switzerland (2001). http://www.who.int/classifications/icf/wha-en.pdf

  17. Johnson M (2006) Recent trends in robot-assisted therapy environments to improve real-life functional performance after stroke. J NeuroEng Rehabil 3(1):29

    Article  PubMed  Google Scholar 

  18. Kearney RE, Stein RB, Parameswaran L (1997) Identification of intrinsic and reflex contributions to human ankle stiffness dynamics. IEEE Trans Biomed Eng 44(6): 493–504. doi:10.1109/10.581944

    Article  Google Scholar 

  19. Kennedy J, Buchan A (2004) Acute neurovascular syndromes: hurry up, please, it’s time. Stroke 35(2):360–362

    Article  PubMed  CAS  Google Scholar 

  20. Klare S, Peer A, Buss M (2010) Development of a 3 DoF MR-Compatible Haptic interface for pointing and reaching movements. Haptics: generating and perceiving tangible sensations, Amsterdam, pp 211–218

  21. Kwakkel G, Kollen B, Krebs H (2008) Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review. Neurorehabil Neural Repair 22(2):111–121

    PubMed  Google Scholar 

  22. Levin M, Dimov M (1997) Spatial zones for muscle coactivation and the control of postural stability. Brain Res 757(1):43–59

    Article  PubMed  CAS  Google Scholar 

  23. Lo A, Guarino P, Richards L, Haselkorn J, Wittenberg G, Federman D, Ringer R, Wagner T, Krebs H, Volpe B et al (2010) Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med 362(19):1772–1783

    Article  PubMed  CAS  Google Scholar 

  24. Loureiro R, Amirabdollahian F, Topping M, Driessen B, Harwin W (2003) Upper limb mediated stroke therapy—GENTLE/s approach special issue on rehabilitation robotics. J Auton Robots 15(1):35–51 Kluwer Academic Publishers

    Article  Google Scholar 

  25. Loureiro R, Amirabdollahian F, Harwin W (2006) A gentle/S approach to robot assisted neuro-rehabilitation. In: Lecture Notes in Control and Information Sciences. Part VI. Robot-assisted neurorehabilitation. Springer, Berlin/Heidelberg, pp 347–363, ISSN 0170-8643

  26. Loureiro R, Johnson M, Harwin W (2006) Collaborative tele-rehabilitation: a strategy for increasing engagement. In: IEEE International conference on biomedical robotics and biomechatronics, pp 859–864

  27. Mak C, Gomes G, Johnson G (2002) A robotic approach to neuro-rehabilitation-interpretation of biomechanical data. In: Seventh international symposium on the 3-D analysis of human movement, centre for life, Newcastle upon Tyne

  28. McCrea P, Eng J, Hodgson A (2003) Linear spring-damper model of the hypertonic elbow: reliability and validity. J Neurosci Methods 128(1-2):121–128

    Article  PubMed  Google Scholar 

  29. 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. Stroke 40(5):e392–e393

    Article  Google Scholar 

  30. Miall R, Jackson J (2006) Adaptation to visual feedback delays in manual tracking—evidence against the Smith predictor model of human visually guided action. Exp Brain Res 172:77–84

    Article  PubMed  CAS  Google Scholar 

  31. Mirbagheri M, Harvey R, Chen D, Rymer W (2003) Identification of reflex and intrinsic mechanical properties in stroke and spinal cord injury. In: Proceedings of the 25’ annual international conference of the IEEE EMBS, pp 1495–1498

  32. Mirbagheri M, Tsao C, Rymer W (2009) Natural history of neuromuscular properties after stroke: a longitudinal study. J Neurol Neurosurg Psychiatry 80(11):1212–1217

    Article  PubMed  CAS  Google Scholar 

  33. Mussa-Ivaldi F (2002) Geometrical principles in motor control. In: Arbib MA (ed) The handbook of brain theory and neural networks, 2nd edition. MIT press, Cambridge, pp 478–482

  34. Osu R, Hirai S, Yoshioka T, Kawato M (2004) Random presentation enables subjects to adapt to two opposing forces on the hand. Nat Neurosci 7(2):111–112

    Article  PubMed  CAS  Google Scholar 

  35. Papadakis M, Buchan A (2009) Approaches to neuroprotective and reperfusion injury therapy. In: Fisher M (ed) Handbook of clinical neurology, vol 94. Elsevier, Amsterdam, pp 1205–1223

  36. Patton J, Stoykov M, Kovic M, Mussa-Ivaldi F (2006) Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors. Exp Brain Res 168(3):368–383

    Article  PubMed  Google Scholar 

  37. Pomeroy VM, Tallis RC (2002) Restoring movement and functional ability after stroke: now and the future. Physiotherapy (London) 88(1):3–17

    Google Scholar 

  38. Prochazka A, Gillard D, Bennett D (1997) Implications of positive feedback in the control of movement. J Neurophysiol 77(6):3237–3251

    PubMed  CAS  Google Scholar 

  39. Rosati G, Gallina P, Masiero S (2007) Design, implementation and clinical tests of a wire-based robot for neurorehabilitation. IEEE Trans Neural Syst Rehabil Eng 15(4):560–569

    Article  PubMed  Google Scholar 

  40. Saka O, Serra V, Samyshkin Y, McGuire A, Wolfe C (2009) Cost-effectiveness of stroke unit care followed by early supported discharge. Stroke 40(1):24

    Article  PubMed  Google Scholar 

  41. Scholz J, Klein M, Behrens T, Johansen-Berg H (2009) Training induces changes in white-matter architecture. Nat Neurosci 12(11):1370–1371. doi:10.1038/nn.2412

    Article  PubMed  CAS  Google Scholar 

  42. Shadmehr R, Mussa-Ivaldi F (1994) Adaptive representation of dynamics during learning of a motor task. J Neurosci 14(5):3208–3224

    PubMed  CAS  Google Scholar 

  43. Stokes EK (2011) A randomised controlled trial of family mediated exercises (fame) following stroke. http://clinicaltrials.gov/ct2/show/NCT00666744

  44. Takahashi C, Reinkensmeyer D (2003) Hemiparetic stroke impairs anticipatory control of arm movement. Exp Brain Res 149(2):131–140

    PubMed  Google Scholar 

  45. Tsuji T, Goto K, Moritani M, Kaneko M, Morasso P (1994) Spatial characteristics of human hand impedance in multi-joint arm movements. In: Proceedings of the IEEE/RSJ/GI international conference on intelligent robots and systems IROS’94—advanced robotic systems and the real world, vol 1, pp 423–430. doi:10.1109/IROS.1994.407441

  46. Wolpert DM, Miali RC, Kawato M (1998) Internal models in the cerebellum. Trends Cogn Sci 2:338

    Article  PubMed  CAS  Google Scholar 

  47. Zhang L, Rymer W (1997) Simultaneous and nonlinear identification of mechanical and reflex properties of human elbow joint muscles. IEEE Trans Biomed Eng 44:1192–1209

    Article  PubMed  CAS  Google Scholar 

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Authors and Affiliations

  1. Cybernetics Research Group, School of Systems Engineering, University of Reading, Reading, RG6 6AY, UK

    W. S. Harwin

  2. Department of Human Movement Sciences, Maastricht University, Maastricht, The Netherlands

    A. Murgia

  3. Department of Physiotherapy, School of Medicine, Trinity College, Dublin, Ireland

    E. K. Stokes

Authors
  1. W. S. Harwin
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  2. A. Murgia
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  3. E. K. Stokes
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Correspondence to W. S. Harwin.

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Harwin, W.S., Murgia, A. & Stokes, E.K. Assessing the effectiveness of robot facilitated neurorehabilitation for relearning motor skills following a stroke. Med Biol Eng Comput 49, 1093–1102 (2011). https://doi.org/10.1007/s11517-011-0799-y

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  • DOI: https://doi.org/10.1007/s11517-011-0799-y

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