• Shane (S.Q.) XieEmail author
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 108)


Robots can be considered as reprogrammable devices which can be used to complete certain tasks in an autonomous manner. While robots have long been used for automation of industrial processes, there is a growing trend where robotic devices are used to provide services for end users.


Ankle Sprain Impedance Control Joint Kinematic Robotic Device Rehabilitation Exercise 
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.


  1. 1.
    G.A. Donnan, M. Fisher, M. Macleod, S.M. Davis, Stroke. The Lancet 371, 1612–1623 (2008)CrossRefGoogle Scholar
  2. 2.
    Annual Report 2009. Stroke Foundation of New Zealand Inc. (2010)Google Scholar
  3. 3.
    M. Khawaja, N. Thomson, Population ageing in New Zealand (2000)Google Scholar
  4. 4.
    R.W. Teasell, L. Kalra, What’s New in stroke rehabilitation. Stroke 35, 383–385 (2004)CrossRefGoogle Scholar
  5. 5.
    V.S. Huang, J.W. Krakauer, Robotic neurorehabilitation: A computational motor learning perspective. J. NeuroEng. Rehabil, 6 (2009)Google Scholar
  6. 6.
    K. Laver, S. George, J. Ratcliffe, M. Crotty, Virtual reality stroke rehabilitation—hype or hope? Aust. Occup. Ther. J. 58, 215–219 (2011)CrossRefGoogle Scholar
  7. 7.
    W.S. Harwin, T. Rahman, R.A. Foulds, A review of design issues in rehabilitation robotics with reference to North American research. IEEE Trans. Rehabil. Eng. 3, 3–13 (1995)CrossRefGoogle Scholar
  8. 8.
    N. Tejima, Rehabilitation robotics: a review. Adv. Robot. 14, 551–564 (2000)CrossRefGoogle Scholar
  9. 9.
    H.I. Krebs, J.J. Palazzolo, L. Dipietro, M. Ferraro, J. Krol, K. Rannekleiv, B.T. Volpe, N. Hogan, Rehabilitation robotics: performance-based progressive robot-assisted therapy. Auton. Robots 15, 7–20 (2003)CrossRefGoogle Scholar
  10. 10.
    S. Hesse, H. Schmidt, C. Werner, A. Bardeleben, Upper and lower extremity robotic devices for rehabilitation and for studying motor control. Curr. Opin. Neurol. 16, 705–710 (2003)CrossRefGoogle Scholar
  11. 11.
    H.I. Krebs, B.T. Volpe, M.L. Aisen, W. Hening, A. Adamovich, H. Poizner, K. Subrahmanyan, N. Hogan, Robotic applications in neuromotor rehabilitation. Robotica 21, 3–11 (2003)CrossRefGoogle Scholar
  12. 12.
    M. Girone, G. Burdea, M. Bouzit, V. Popescu, J.E. Deutsch, Stewart platform-based system for ankle telerehabilitation. Auton. Robots 10, 203–212 (2001)CrossRefzbMATHGoogle Scholar
  13. 13.
    H.I. Krebs, B.T. Volpe, M.L. Aisen, N. Hogan, Increasing productivity and quality of care: Robot-aided neuro-rehabilitation. J. Rehabil. Res. Dev. 37, 639–652 (2000)Google Scholar
  14. 14.
    J.A. Saglia, N.G. Tsagarakis, J.S. Dai, D.G. Caldwell, Control strategies for ankle rehabilitation using a high performance ankle exerciser, in IEEE International Conference on Robotics and Automation (2010), pp. 2221–2227Google Scholar
  15. 15.
    R. Riener, M. Frey, M. Bernhardt, T. Nef, G. Colombo, Human-centered rehabilitation robotics, in IEEE International Conference on Rehabilitation Robotics (2005), pp. 319–322Google Scholar
  16. 16.
    A. Duschau-Wicke, J. Von Zitzewitz, A. Caprez, L. Lunenburger, R. Riener, Path control: a method for patient-cooperative robot-aided gait rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 38–48 (2010)CrossRefGoogle Scholar
  17. 17.
    A. Roy, H.I. Krebs, S.L. Patterson, T.N. Judkins, I.K. Larry, R.M. Macko, N. Hogan, Measurement of human ankle stiffness using the anklebot, in International Conference on Rehabilitation Robotics (2007), pp. 356–363Google Scholar
  18. 18.
    H. Vallery, A. Duschau-Wicke, R. Riener, Generalized elasticities improve patient-cooperative control of rehabilitation robots, in 2009 IEEE International Conference on Rehabilitation Robotics, ICORR 2009 (2009), pp. 535–541Google Scholar
  19. 19.
    L. Marchal-Crespo, D.J. Reinkensmeyer, Review of control strategies for robotic movement training after neurologic injury. J. NeuroEng. Rehabil. 6 (2009)Google Scholar
  20. 20.
    T. Nef, M. Mihelj, R. Riener, ARMin: a robot for patient-cooperative arm therapy. Med. Biol. Eng. Compu. 45, 887–900 (2007)CrossRefGoogle Scholar
  21. 21.
    J.E. Deutsch, J. Latonio, G. Burdea, R. Boian, Post-stroke rehabilitation with the Rutgers Ankle System: a case study. Presence 10, 416–430 (2001)CrossRefGoogle Scholar
  22. 22.
    D.J. Reinkensmeyer, J.L. Emken, S.C. Cramer, Robotics, motor learning, and neurologic recovery. Annu. Rev. Biomed. Eng. 6, 497–525 (2004)CrossRefGoogle Scholar
  23. 23.
    H.I. Krebs, D. Williams, J. Celestino, S.K. Charles, D. Lynch, N. Hogan, Robot-aided neurorehabilitation: a robot for wrist rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 15, 327–335 (2007)CrossRefGoogle Scholar
  24. 24.
  25. 25.
    R. Riener, L. Lunenburger, S. Jezernik, M. Anderschitz, G. Colombo, V. Dietz, Patient-cooperative strategies for robot aided treadmill training: first experimental results. IEEE Trans. Neural Syst. Rehabil. Eng. 13, 380–394 (2005)CrossRefGoogle Scholar
  26. 26.
    S. Jezernik, G. Colombo, M. Morari, Automatic gait-pattern adaptation for rehabilitation with 4-dof robotic orthosis. IEEE Trans. Robot. Autom. 20, 574–582 (2004)CrossRefGoogle Scholar
  27. 27.
    J.A. Blaya, H. Herr, Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait. IEEE Trans. Neural Syst. Rehabil. Eng. 12, 24–31 (2004)CrossRefGoogle Scholar
  28. 28.
    N. Hogan, Stable execution of contact tasks using impedance control, in IEEE International Conference on Robotics and Automation (1987), pp. 1047–1054Google Scholar
  29. 29.
    N. Hogan, S.P. Buerger, Impedance and interaction control, in Robotics and Automation Handbook, ed. by T. Kurfess (CRC Press, New York, 2005)Google Scholar
  30. 30.
    H.S. Lo, S.Q. Xie, Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects. Med. Eng. Phys. 34, 261–268 (2012)CrossRefGoogle Scholar
  31. 31.
    R.A.R.C. Gopura, K. Kiguchi, Mechanical designs of active upper-limb exoskeleton robots state-of-the-art and design difficulties, in IEEE International Conference on Rehabilitation Robotics (2009), pp. 178–187Google Scholar
  32. 32.
    M. Dettwyler, A. Stacoff, I.A. Kramers-de Quervain, E. Stussi, Modelling of the ankle joint complex. Reflections with regards to ankle prostheses. Foot Ankle Surg. 10, 109–119 (2004)Google Scholar
  33. 33.
  34. 34.
    G. Zeng, A. Hemami, An overview of robot force control. Robotica 15, 473–482 (1997)CrossRefGoogle Scholar
  35. 35.
    M.R. Safran, J.E. Zachazewski, R.S. Benedetti, A.R.I. Bartolozzi, R. Mandelbaum, Lateral ankle sprains: a comprehensive review Part 2: treatment and rehabilitation with an emphasis on the athlete. Med. Sci. Sports Exerc. 31, S438–S447 (1999)CrossRefGoogle Scholar
  36. 36.
    C.G. Mattacola, M.K. Dwyer, Rehabilitation of the ankle after acute sprain or chronic instability. J. Athletic Training 37, 413–429 (2002)Google Scholar
  37. 37.
    B. Siciliano, L. Villani, Robot force control (Kluwer Academic Publishers, Boston, 1999)CrossRefzbMATHGoogle Scholar
  38. 38.
    T. Lefebvre, J. Xiao, H. Bruyninckx, G. Gersem, Active compliant motion: a survey. Adv. Robot. 19, 479–499 (2005)CrossRefGoogle Scholar
  39. 39.
    M.H. Raibert, J.J. Craig, Hybrid position/force control of manipulators. J. Dyn. Syst. Meas. Control Trans. ASME 103, 126–133 (1981)CrossRefGoogle Scholar
  40. 40.
    N. Hogan, Impedance control: an approach to manipulation: Parts I, II and III. J. Dyn. Syst. Meas. Contr. 107, 17–24 (1985)CrossRefzbMATHGoogle Scholar
  41. 41.
    A. Roy, H.I. Krebs, D. Williams, C.T. Bever, L.W. Forrester, R.M. Macko, N. Hogan, Robot-aided neurorehabilitation: a novel robot for ankle rehabilitation. IEEE Trans. Rob. 25, 569–582 (2009)CrossRefGoogle Scholar
  42. 42.
    J. Yoon, J. Ryu, K.-B. Lim, Reconfigurable ankle rehabilitation robot for various exercises. J. Rob. Syst. 22, S15–S33 (2006)CrossRefGoogle Scholar
  43. 43.
    J.A. Saglia, N.G. Tsagarakis, J.S. Dai, D.G. Caldwell, A high-performance redundantly actuated mechanism for ankle rehabilitation. Int. J. Rob. Res. 28, 1216–1227 (2009)CrossRefGoogle Scholar
  44. 44.
    M. Bernhardt, M. Frey, G. Colombo, T. Rahman, Hybrid force-position control yields cooperative behaviour of the rehabilitation robot LOKOMAT, in IEEE International Conference on Rehabilitation Robotics (2005), pp. 536–539Google Scholar
  45. 45.
    R. Colbaugh, H. Seraji, K. CGlass, Direct adaptive impedance control of robot manipulators. J. Rob. Syst. 10, 217–248 (1993)Google Scholar
  46. 46.
    C.-C. Cheah, D. Wang, Learning impedance control for robotic manipulators. IEEE Trans. Robot. Autom. 14, 452–465 (1998)CrossRefGoogle Scholar
  47. 47.
    S.K. Singh, D.O. Popa, Analysis of some fundamental problems in adaptive control of force and impedance behavior: theory and experiments. IEEE Trans. Robot. Autom. 11, 912–921 (1995)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.The Department of Mechanical EngineeringThe University of AucklandAucklandNew Zealand

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