Upper Limb Exoskeleton Control for Isotropic Sensitivity of Human Arm

  • Rok GoljatEmail author
  • Tadej Petrič
  • Jan Babič
Conference paper
Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 16)


Most of today’s assistive devices are controlled to provide uniform assistance irrespectively from the configuration of the human arm and the direction of the movement. We propose an innovative control method for arm exoskeletons that takes into account both of these parameters and compensates the anisotropic property of the force manipulability measure, intrinsic to the biomechanics of the human arm. To test our controller we designed a set of reaching tasks where the subjects had to carry two different loads to targets at five different locations and of two different sizes. Reaching times and trajectories were analysed for the evaluation of the controller. Through the analysis of the average reaching times we found that our method successfully enhances the motion while the analysis of the average maximal deviation from the ideal trajectories showed that our method does not induce any additional dynamic behaviour to the user.


Mobility Measure Baseline Session Assistive Force Manipulability Measure Limb Exoskeleton 
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  1. 1.
    Dollar, A.M., Herr, H.: Lower extremity exoskeletons and active orthoses: challenges and state-of-the-art. IEEE Trans. Robot. 24, 144–158 (2008)CrossRefGoogle Scholar
  2. 2.
    Peternel, L., Noda, T., Petrič, T., Ude, A., Morimoto, J., Babič, J.: Adaptive control of exoskeleton robots for periodic assistive behaviours based on EMG feedback minimisation. PLoS ONE 11(2), 02 (2016)CrossRefGoogle Scholar
  3. 3.
    Peternel, L., Petrič, T., Oztop, E., Babič, J.: Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach. Auton Rob 36(1), 123–136 (2013)Google Scholar
  4. 4.
    Cos, I., Belanger, N., Cisek, P.: The influence of predicted arm biomechanics on decision making. J. Neurophysiol. 105(6), 3022–3033, Jun 2011Google Scholar
  5. 5.
    Yamashita, M.: Robotic rehabilitation system for human upper limbs using guide control and manipulability ellipsoid prediction. Procedia Technol. 15, 559–565 (2014)CrossRefGoogle Scholar
  6. 6.
    Yoshikawa, T.: Foundations of robotics: analysis and control. MIT Press, Cambridge, MA (1990)Google Scholar
  7. 7.
    Sabes, P.N., Jordan, M.I.: Obstacle avoidance and a perturbation sensitivity model for motor planning. J. Neurosci.: Off. J. Soc. Neurosci. 17(18), 7119–7128, Sept. 1997Google Scholar
  8. 8.
    Hogan, N.: Impedance control: an approach to manipulation: part ii implementation. J. Dyn. Syst. Meas. Control 107(1), 8–16 (1985)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Automation, Biocybernetics and Robotics DepartmentJozef Stefan InstituteLjubljanaSlovenia

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