A Wearable Rehabilitation Robotic Hand Driven by PM-TS Actuators

  • Jun Wu
  • Jian Huang
  • Yongji Wang
  • Kexin Xing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6425)


Robotic-assisted therapy is of great benefit to the recovery of motor function for the patients survived from stroke. However there have been few emphases on the patients’ hand training/exercise during the rehabilitation process. The goal of this research is to develop a novel wearable device for robotic assisted hand therapy. Unlike the traditional agonist/antagonist PM actuator, we propose a new PM-TS actuator comprising a Pneumatic Muscle (PM) and a Torsion Spring (TS) for joint drive. Based on the proposed PM-TS actuator, we design a robotic hand which is wearable and provides assistive forces required for finger training. The robotic hand has two distinct degrees of freedom at the metacarpophalangeal (MP) and proximal interphalangeal (PIP) joints. The variable integral PID (VIPID) controller was designed to make the joint angle of robotic hand can accurately track a given trajectory. The results show that the VIPID controller has better performance than the conventional PID controller. The proposed rehabilitation robotic hand is potentially of providing supplemental at-home therapy in addition to the clinic treatment.


Rehabilitation Robotic Hand Pneumatic Muscle (PM) Torsion Spring (TS) PM-TS Actuator 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bouzit, M., Burdea, G., Popescu, G., Boian, R.: The Rutgers Master II—New design force-feedback glove. IEEE ASME Trans. Mechatron. 7, 256–263 (2002)CrossRefGoogle Scholar
  2. 2.
    Takahashi, C.D., Der-Yeghiaian, L., Le, V.H., Cramer, S.C.: A robotic device for hand motor therapy after stroke. In: 9th IEEE International Conference on Rehabilitation Robotics Conference, pp. 17–20. IEEE Press, Chicago (2005)Google Scholar
  3. 3.
    Worsnopp, T.T., Peshkin, M.A., Colgate, J.E., Kamper, D.G.: An actuated finger exoskeleton for hand rehabilitation following stroke. In: 10th IEEE International Conference on Rehabilitation Robotics, pp. 896–901. IEEE Press, Noordwijk (2007)Google Scholar
  4. 4.
    Loureiro, R.C., Harwin, W.S.: Reach & grasp therapy: Design and control of a 9-DOF robotic neuro-rehabilitation system. In: 10th IEEE International Conference on Rehabilitation Robotics, pp. 757–763. IEEE Press, Noordwijk (2007)Google Scholar
  5. 5.
    Dovat, L., Lambercy, O., Johnson, V., Salman, B., Wong, S., Gassert, R., Burdet, E., Leong, T.C., Milner, T.: A cable driven robotic system to train finger function after stroke. In: 10th IEEE International Conference on Rehabilitation Robotics, pp. 222–227. IEEE Press, Noordwijk (2007)Google Scholar
  6. 6.
    Lambercy, O., Dovat, L., Gassert, R., Burdet, E., Chee, L.T., Milner, T.: A Haptic Knob for Rehabilitation of Hand Function. IEEE Trans. Neural Syst. Rehabil. Eng. 15, 356–366 (2007)CrossRefGoogle Scholar
  7. 7.
    Mulas, M., Folgheraiter, M., Gini, G.: An EMG-controlled Exoskeleton for Hand Rehabilitation. In: 9th IEEE International Conference on Rehabilitation Robotics, pp. 371–374. IEEE Press, Chicago (2005)Google Scholar
  8. 8.
    Tsagarakis, N.G., Caldwell, D.G.: Development and Control of a ‘Soft-Actuated’ Exoskeleton for Use in Physiotherapy and Training. Autonomous Robots 15, 21–33 (2003)CrossRefGoogle Scholar
  9. 9.
    Caldwell, D.G., Medrano-Cerda, G.A., Goodwin, M.: Control of pneumatic muscle actuators. IEEE Control Syst. Mag. 15, 40–48 (1995)CrossRefGoogle Scholar
  10. 10.
    Chou, C.P., Hannaford, B.: Static and dynamic characteristics of McKibben pneumatic artificial muscles. In: IEEE Robotics and Automation Conf., pp. 281–286. IEEE Press, San Diego (1994)Google Scholar
  11. 11.
    Ferris, D.P., Czerniecki, J.M., Hannaford, B.: An Ankle-Foot Orthosis Powered by Artificial Pneumatic Muscles. J. Appl. Biomech. 21, 189–197 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jun Wu
    • 1
  • Jian Huang
    • 1
  • Yongji Wang
    • 1
  • Kexin Xing
    • 1
  1. 1.Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and EngineeringHuazhong University of Science and TechnologyWuhanChina

Personalised recommendations