Medical & Biological Engineering & Computing

, Volume 47, Issue 4, pp 441–447 | Cite as

Improving backdrivability in geared rehabilitation robots

Technical Note


Many rehabilitation robots use electric motors with gears. The backdrivability of geared drives is poor due to friction. While it is common practice to use velocity measurements to compensate for kinetic friction, breakaway friction usually cannot be compensated for without the use of an additional force sensor that directly measures the interaction force between the human and the robot. Therefore, in robots without force sensors, subjects must overcome a large breakaway torque to initiate user-driven movements, which are important for motor learning. In this technical note, a new methodology to compensate for both kinetic and breakaway friction is presented. The basic strategy is to take advantage of the fact that, for rehabilitation exercises, the direction of the desired motion is often known. By applying the new method to three implementation examples, including drives with gear reduction ratios 100–435, the peak breakaway torque could be reduced by 60–80%.


Backdrivability Friction Breakaway friction Human–robot interaction Rehabilitation robotics 


  1. 1.
    Canudas de Wit C, Noel P, Aubin A, Brogliato B (1991) Adaptive friction compensation in robot manipulators: low velocities. Int J Robot Res 10(3):189–199. doi:10.1177/027836499101000301 CrossRefGoogle Scholar
  2. 2.
    Carignan C, Liszka M, Roderick S (2005) Design of an arm exoskeleton with scapula motion for shoulder rehabilitation. In: Proceedings of the 12th IEEE international conference on advanced robotics ICAR, July 18–20, Seattle, pp 524–531. doi:10.1109/ICAR.2005.1507459
  3. 3.
    Dromerick AW, Lum PS, Hidler J (2006) Activity-based therapies. NeuroRX 3(4):428–438. doi:10.1016/j.nurx.2006.07.004 CrossRefGoogle Scholar
  4. 4.
    Hauschild JP, Heppler GR (2007) Control of harmonic drive motor actuated flexible linkages. In: IEEE international conference on robotics and automation, 10–14 April, Rome, pp 3451–3456. doi: 10.1109/ROBOT.2007.364006
  5. 5.
    Kermani MR, Patel RV, Moallem M (2007) Friction identification and compensation in robotic manipulators. IEEE Trans Instrum Meas 56(6):2346–2353. doi:10.1109/TIM.2007.907957 CrossRefGoogle Scholar
  6. 6.
    Krebs HI, Ferraro M, Buerger SP, Newbery MJ, Makiyama A, Sandmann M, Lynch D, Volpe BT, Hogan N (2004) Rehabilitation robotics: pilot trial of a spatial extension for MIT-manus. J Neuroeng Rehabil 1:5. doi:10.1186/1743-0003-1-5 CrossRefGoogle Scholar
  7. 7.
    Lagarias JC, Reeds JA, Wright MH, Wright PE (1998) Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM J Optim 9(1):112–147MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Lotze M, Braun C, Birbaumer N, Anders S, Cohen L (2003) Motor learning elicited by voluntary drive. Brain 126(4):866–872. doi:10.1093/brain/awg079 CrossRefGoogle Scholar
  9. 9.
    Nef T, Riener R (2008) Shoulder actuation mechanisms for arm rehabilitation exoskeletons. In: Proceedings of the second IEEE/RAS-EMBS international conference on biomedical robotics and biomechatronics, October 19–22, ScottsdaleGoogle Scholar
  10. 10.
    Nef T, Mihelj M, Riener R (2007) ARMin—a robot for patient-cooperative arm therapy. Med Biol Eng Comput 45(9):887–900. doi:10.1007/s11517-007-0226-6 CrossRefGoogle Scholar
  11. 11.
    Rosen J, Perry JC, Manning N, Burns S, Hannaford B (2005) The human arm kinematics and dynamics during daily activities—toward a 7 DOF upper limb powered exoskeleton. In: Proceedings of the 12th IEEE international conference on advanced robotics ICAR, July 18–20, USA, pp 532–539. doi:10.1109/ICAR.2005.1507460
  12. 12.
    Selmic RR, Lewis FL (2000) Deadzone compensation in motion control systems using neural networks. IEEE Trans Autom Control 45(4):602–613. doi:10.1109/9.847098 MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Selmic RR, Lewis FL (2002) Neural-network approximation of piecewise continuous functions: application to friction compensation. IEEE Trans Neural Netw 13(3):745–751. doi:10.1109/TNN.2002.1000141 CrossRefGoogle Scholar
  14. 14.
    Zhang LQ, Park HS, Ren Y (2007) Developing an intelligent robotic arm for stroke rehabilitation. In: Proceedings of the 10th IEEE international conference on rehabilitation robotics ICORR, June 12–15, Noordwijk, pp 984–993. doi:10.1109/ICORR.2007.4428543

Copyright information

© International Federation for Medical and Biological Engineering 2009

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringThe Catholic University of AmericaWashington DCUSA
  2. 2.Center for Applied Biomechanics and Rehabilitation ResearchNational Rehabilitation HospitalWashington DCUSA

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