A Cost-Efficient Tele-rehabilitation Device for Training Distal Upper Limb Functions After Stroke

  • Patrick Weiss
  • Alexander Gabrecht
  • Marcus Heldmann
  • Achim Schweikard
  • Erik Maehle
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 515)


Robotic rehabilitation devices offer prospects in improving the therapy outcome in stroke patients. In particular the combination with tele-rehabilitation functionality may be beneficial to reduce cost, which is especially required for home-based rehabilitation. In this paper a device is presented that allows for exercising supination/pronation, dorsiflexion, and finger training. Its communication architecture follows a modular design approach. The Qt-based graphical UI can be executed on different operating systems and devices including the cost-effective Rasperry Pi single-board computer. Tele-rehabilitation functionality is implemented based on SSL-encrypted RESTful web services following a three-tier architecture. Cost is reduced by omitting expensive sensors. A torque sensor is replaced with current-based torque sensing, used for progress measurement and interactive exercises. The evaluation shows accurate results after compensating the static friction, justifying the omission of an additional torque sensor. Torque measurements during passive exercises showed higher and more asymmetric ratings for a stroke patient than for a healthy subject indicating that this measurement may be used as an estimator of spasticity.


Robotic rehabilitation Tele-rehabilitation Stroke Home health care Distal upper limb functions Motor control 


  1. 1.
    Allington, J., Spencer, S. J., Klein, J., Buell, M., Reinkensmeyer, D. J., Bobrow, J.: Supinator extender (SUE): a pneumatically actuated robot for forearm/wrist rehabilitation after stroke. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1579–1582. IEEE (2011)Google Scholar
  2. 2.
    Balasubramanian, S., Klein, J., Burdet, E.: Robot-assisted rehabilitation of hand function. Curr. Opin. Neurol. 23(6), 661 (2010)CrossRefGoogle Scholar
  3. 3.
    Brewer, B., Klatzky, R., Matsuoka, Y.: Feedback distortion to overcome learned nonuse: a system overview. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, pp. 1613–1616. IEEE (2003)Google Scholar
  4. 4.
    Brewer, B., Klatzky, R., Matsuoka, Y.: Visual feedback distortion in a robotic environment for hand rehabilitation. Brain Res. Bull. 75(6), 804–813 (2008)CrossRefGoogle Scholar
  5. 5.
    Hesse, S., Schulte-Tigges, G., Konrad, M., Bardeleben, A., Werner, C.: Robot-assisted arm trainer for the passive and active practice of bilateral forearm and wrist movements in hemiparetic subjects. Arch. Phys. Med. Rehabil. 84(6), 915–920 (2003)CrossRefGoogle Scholar
  6. 6.
    Kolominsky-Rabas, P.L., Heuschmann, P.U., Marschall, D., Emmert, M., Baltzer, N., Neundörfer, B., Schöffski, O., Krobot, K.J., et al.: Lifetime cost of ischemic stroke in germany: results and national projections from a population-based stroke registry the erlangen stroke project. Stroke 37(5), 1179–1183 (2006)CrossRefGoogle Scholar
  7. 7.
    Krebs, H.I., Volpe, B.T., Williams, D., Celestino, J., Charles, S.K., Lynch, D., Hogan, N.: Robot-aided neurorehabilitation: a robot for wrist rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 15(3), 327–335 (2007)CrossRefGoogle Scholar
  8. 8.
    Kwakkel, G., Kollen, B.J., Krebs, H.I.: Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review. Neurorehabil. Neural Repair 22(2), 111–121 (2008)CrossRefGoogle Scholar
  9. 9.
    Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Toy, A., Leonhardt, S., et al.: A survey on robotic devices for upper limb rehabilitation. J. Neuroeng. Rehabil. 11(1), 3 (2014)CrossRefGoogle Scholar
  10. 10.
    Matsuoka, Y., Allin, S., Klatzky, R.: The tolerance for visual feedback distortions in a virtual environment. Physiol. Behav. 77(4–5), 651–655 (2002)CrossRefGoogle Scholar
  11. 11.
    Metzger, J.-C., Lambercy, O., Chapuis, D., Gassert, R.: Design and characterization of the ReHapticKnob, a robot for assessment and therapy of hand function. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3074–3080. IEEE (2011)Google Scholar
  12. 12.
    Nakayama, H., Jørgensen, H.S., Raaschou, H.O., Olsen, T.S.: Recovery of upper extremity function in stroke patients: the Copenhagen stroke study. Age (SD) 74, 11–2 (1994)Google Scholar
  13. 13.
    Oblak, J., Cikajlo, I., Matjacic, Z.: Universal haptic drive: a robot for arm and wrist rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 18(3), 293–302 (2010)CrossRefGoogle Scholar
  14. 14.
    Reinkensmeyer, D.J., Emken, J.L., Cramer, S.C.: Robotics, motor learning, and neurologic recovery. Annu. Rev. Biomed. Eng. 6, 497–525 (2004)CrossRefGoogle Scholar
  15. 15.
    Takahashi, C.D., Der-Yeghiaian, L., Le, V., Motiwala, R.R., Cramer, S.C.: Robot-based hand motor therapy after stroke. Brain 131(2), 425–437 (2008)CrossRefGoogle Scholar
  16. 16.
    Van der Lee, J.H., de Groot, V., Beckerman, H., Wagenaar, R.C., Lankhorst, G.J., Bouter, L.M.: The intra-and interrater reliability of the action research arm test: a practical test of upper extremity function in patients with stroke. Arch. Phys. Med. Rehabil. 82(1), 14–19 (2001)CrossRefGoogle Scholar
  17. 17.
    Warlow, C., Van Gijn, J., Sandercock, P., Hankey, G., Dennis, M., Bamford, J., Wardlaw, J., Sudlow, C., Rinkel, G., Rothwell, P.: Stroke: practical management (2008)Google Scholar
  18. 18.
    Weiss, P., Heldmann, M. Gabrecht, A. Schweikard, A., Münte, T. M., Maehle, E.: A low cost tele-rehabilitation device for training of wrist and finger functions after stroke. In: Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth 2014), pp. 422–425 (2014)Google Scholar
  19. 19.
    Weiss, P., Heldmann, M., Münte, T., Schweikard, A., Maehle, E.: A rehabilitation system for training based on visual feedback distortion. In: Pons, J.L., Torricelli, D., Pajaro, M. (eds.) Converging Clinical and Engineering Research on Neurorehabilitation, pp. 297–302. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Patrick Weiss
    • 1
  • Alexander Gabrecht
    • 1
  • Marcus Heldmann
    • 2
  • Achim Schweikard
    • 3
  • Erik Maehle
    • 1
  1. 1.Institute of Computer EngineeringUniversity of LübeckLübeckGermany
  2. 2.University Medical Center Schleswig-HolsteinUniversity of LübeckLübeckGermany
  3. 3.Institute for Robotics and Cognitive SystemsUniversity of LübeckLübeckGermany

Personalised recommendations