IJAT Vol.11 No.3 pp. 442-449
doi: 10.20965/ijat.2017.p0442


Elbow Musculoskeletal Model for Industrial Exoskeleton with Modulated Impedance Based on Operator’s Arm Stiffness

Daniele Borzelli, Stefano Pastorelli, and Laura Gastaldi

Politecnico di Torino
Corso Duca degli Abruzzi 24, 10129 Torino, Italy

Corresponding author

October 1, 2016
April 10, 2017
Online released:
April 28, 2017
May 5, 2017
arm stiffness, hill muscle model, elbow model, exoskeleton
With the ageing of the workforce in the manufacturing industry, the possibility of introducing support aids such as exoskeletons to reduce the fatigue and effort of the operator has to be evaluated. An upper-limb exoskeleton with controlled impedance is expected to reduce the discomfort in the operations which require precision. Hence, arm joint stiffening is required. Real-time calculation of the exoskeleton impedance should be based on the operator’s limb impedance, evaluated through electromyographic signals. A model of the operator’s arm is necessary to identify the best control law for the exoskeleton. In this paper, preliminary considerations and a model of the elbow on which two muscles act as agonist-antagonist are presented. Numerical results are discussed, and an estimation of the performance is also proposed.
Cite this article as:
D. Borzelli, S. Pastorelli, and L. Gastaldi, “Elbow Musculoskeletal Model for Industrial Exoskeleton with Modulated Impedance Based on Operator’s Arm Stiffness,” Int. J. Automation Technol., Vol.11 No.3, pp. 442-449, 2017.
Data files:
  1. [1] E. E. Cavallaro, J. Rosen, J. C. Perry, and S. Burns, “Real-time myoprocessors for a neural controlled powered exoskeleton arm,” IEEE Trans. Biomed. Eng., Vol.53, pp. 2387-2396, November 2006.
  2. [2] J. Rosen, M. Brand, M. B. Fuchs, and M. Arcan, “A myosignal-based powered exoskeleton system,” IEEE Trans. Syst. Man, Cybern. - Part A Syst. Humans, Vol.31, pp. 210-222, May 2001.
  3. [3] E. Rocon, J. M. Belda-Lois, A. F. Ruiz, M. Manto, J. C. Moreno, and J. L. Pons, “Design and validation of a rehabilitation robotic exoskeleton for tremor assessment and suppression,” IEEE Trans. Neural Syst. Rehabil. Eng., Vol.15, pp. 367-378, September 2007.
  4. [4] B. J. Makinson, “Research and Development Prototype for Machine Augmentation of Human Strength and Endurance. Hardiman I Project,” General Electric Report S-71-1056, Schenectady, NY, May 1971.
  5. [5] A. Young and D. Ferris, “State-of-the-art and Future Directions for Robotic Lower Limb Exoskeletons,” IEEE Trans. Neural Syst. Rehabil. Eng., Vol.25, No.2, pp. 171-182, February 2017.
  6. [6] A. B. Zoss, H. Kazerooni, and A. Chu, “Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX),” IEEE/ASME Trans. Mechatr., Vol.11, pp. 128-138, April 2006.
  7. [7] S. K. Banala, S. H. Kim, S. K. Agrawal, and J. P. Scholz, “Robot assisted gait training with active leg exoskeleton (ALEX),” IEEE Trans. Neural Syst. Rehab. Eng., Vol.17, pp. 2-8, Feb. 2009.
  8. [8] T. Nef, M. Mihelj, and R. Riener, “ARMin: a robot for patient-cooperative arm therapy,” Med. Biol. Eng. Comput., Vol.45, pp. 887-900, September 2007.
  9. [9] S. Zhou, D. L. Lawson, W. E. Morrison, and I. Fairweather, “Electromechanical delay in isometric muscle contractions evoked by voluntary, reflex and electrical stimulation,” Eur. J. Appl. Physiol. Occup. Physiol., Vol.70, No.2, pp. 138-145, March 1995.
  10. [10] K. Sacco, F. Cauda, F. D’Agata, S. Duca, M. Zettin, R. Virgilio et al., “A combined robotic and cognitive training for locomotor rehabilitation: evidences of cerebral functional reorganization in two chronic traumatic brain injured patients,” Front. Hum. Neurosci., Vol.5, p. 146, November 2011.
  11. [11] J. van der Vorm, R. Nugent, and L. O’Sullivan, “Safety and Risk Management in Designing for the Lifecycle of an Exoskeleton: A Novel Process Developed in the Robo-Mate Project,” Procedia Manuf., Vol.3, pp. 1410-1417, July 2015.
  12. [12] S. Spada, L. Ghibaudo, S. Gilotta, L. Gastaldi, and M. P. Cavatorta, “Measurement procedure of parameter to assess an exoskeleton introduction in industrial reality: main issues and EAWS risk assessment,” Proc. Manuf., 2017.
  13. [13] N. Sylla, V. Bonnet, F. Colledani, and P. Fraisse, “Ergonomic contribution of ABLE exoskeleton in automotive industry,” Int. J. Ind. Ergon., Vol.44, pp. 475-481, July 2014.
  14. [14] T. Flash, “The control of hand equilibrium trajectories in multi-joint arm movements,” Biol. Cybern., Vol.57, pp. 257-274, November 1987.
  15. [15] E. Burdet, R. Osu, D. Franklin, T. Milner, and M. Kawato, “The central nervous system stabilizes unstable dynamics by learning optimal impedance,” Nature, Vol.414, pp. 446-449, November 2001.
  16. [16] R. Shadmehr, F. A. Mussa-Ivaldi, and E. Bizzi, “Postural force fields of the human arm and their role in generating multijoint movements,” J. Neurosci., Vol.13, pp. 45-62, January 1993.
  17. [17] E. J. Perreault, R. F. Kirsch, and P. E. Crago, “Voluntary Control of Static Endpoint Stiffness During Force Regulation Tasks,” J Neurophysiol, Vol.87, pp. 2808-2816, June 2002.
  18. [18] D. Franklin and T. Milner, “Adaptive control of stiffness to stabilize hand position with large loads,” Exp. Brain Res., Vol.152, No.2, pp. 211-220, July 2003.
  19. [19] N. Hogan, “Impedance control: An approach to manipulation,” Am. Control Conf. 1984, June 1984.
  20. [20] T. Yoshikawa, “Manipulability of Robotic Mechanisms,” Int. J. Rob. Res., Vol.4, pp. 3-9, June 1985.
  21. [21] M. Verotti, P. Masarati, M. Morandini, and N. P. Belfiore, “Isotropic compliance in the Special Euclidean Group SE(refeq3),” Mech. Mach. Theory, Vol.98, pp. 263-281, April 2016.
  22. [22] M. Verotti and N. P. Belfiore, “Isotropic Compliance in E(3): Feasibility and Workspace Mapping,” J. Mech. Robot., Vol.8, No.6, September 2016.
  23. [23] F. Mussa-Ivaldi, N. Hogan, and E. Bizzi, “Neural, mechanical, and geometric factors subserving arm posture in humans,” J. Neurosci., Vol.5, pp. 2732-2743, October 1985.
  24. [24] P. L. Gribble, L. I. Mullin, N. Cothros, and A. Mattar, “Role of cocontraction in arm movement accuracy,” J. Neurophysiol, Vol.89, No.5, pp. 2396-2405, May 2003.
  25. [25] R. Osu, N. Kamimura, H. Iwasaki, E. Nakano, C. M. Harris, Y. Wada, and M. Kawato, “Optimal impedance control for task achievement in the presence of signal-dependent noise,” J. Neurophysiol, Vol.92, No.2, pp. 1199-1215, August 2004.
  26. [26] H. Gomi and R. Osu, “Task-dependent viscoelasticity of human multijoint arm and its spatial characteristics for interaction with environments,” J. Neurosci., Vol.18, pp. 8965-8978, November 1998.
  27. [27] R. Osu and H. Gomi, “Multijoint muscle regulation mechanisms examined by measured human arm stiffness and EMG signals,” J. Neurophysiol., Vol.81, pp. 1458-1468, April 1999.
  28. [28] M. Mauro, S. Mohtar, T. Pastorelli, and S. Sorli, “Pre-design of an active central mechanism for space docking,” 67th Int. Astronautical Congr. (IAC 2016), October 2016.
  29. [29] J. F. Veneman, R. Kruidhof, E. E. G. Hekman, R. Ekkelenkamp, E. H. F. Van Asseldonk, and H. van der Kooij, “Design and Evaluation of the LOPES Exoskeleton Robot for Interactive Gait Rehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng., Vol.15, pp. 379-386, September 2007.
  30. [30] T. Lenzi, N. Vitiello, S. M. M. De Rossi, S. Roccella, F. Vecchi, and M. C. Carrozza, “NEUROExos: A variable impedance powered elbow exoskeleton,” 2011 IEEE Int. Conf. on Robotics and Automation, pp. 1419-1426, May 2011.
  31. [31] A. Ajoudani, N. Tsagarakis, and A. Bicchi, “Tele-impedance: Teleoperation with impedance regulation using a body-machine interface,” Int. J. Rob. Res., Vol.31, pp. 1-14, October 2012.
  32. [32] P. Liang, C. Yang, N. Wang, Z. Li, R. Li, and E. Burdet, “Implementation and test of human-operated and human-like adaptive impedance controls on Baxter robot,” Conf. on Advances in Autonomous Robotics Systems (TAROS 2014), pp. 109-119, September 2014.
  33. [33] M. Ison and P. Artemiadis, “Multi-directional impedance control with electromyography for compliant human-robot interaction,” IEEE Int. Conf. Rehab. Robotics, pp. 416-421, October 2015.
  34. [34] A. A. Blank, A. M. Okamura, and L. L. Whitcomb, “Task-dependent impedance and implications for upper-limb prosthesis control,” Int. J. Rob.control, Vol.33, pp. 827-846, May 2014.
  35. [35] S. Lee and Y. Sankai, “Virtual impedance adjustment in unconstrained motion for an exoskeletal robot assisting the lower limb,” Adv. Robot., Vol.19, pp. 773-795, January 2005.
  36. [36] F. Lacquaniti, M. Carrozzo, and N. A. Borghese, “Time-varying mechanical behavior of multijointed arm in man,” J. Neurophysiol., Vol.69, pp. 1443-1464, May 1993.
  37. [37] H. Gomi and M. Kawato, “Equilibrium-point control hypothesis examined by measured arm stiffness during multijoint movement,” Science, Vol.272, pp. 117-120, April 1996.
  38. [38] L. P. J. Selen, D. W. Franklin, and D. M. Wolpert, “Impedance control reduces instability that arises from motor noise,” J. Neurosci., Vol.29, pp. 12606-12616, October 2009.
  39. [39] D.-Z. Chen and K.-L. Yao, “Drive train design of redundant-drive backlash-free robotic mechanisms,” Mech. Mach. Theory, Vol.35, pp. 1269-1285, September 2000.
  40. [40] D.-Z. Chen, C.-P. Liu, and D.-W. Duh, “On the conceptual design of redundant-drive backlash-free geared robot manipulators,” Mech. Mach. Theory, Vol.37, pp. 3-14, January 2002.
  41. [41] A. V. Hill, “The Mechanics of Active Muscle,” Proc. R. Soc. B Biol. Sci., Vol.141, pp. 104-117, March 1953.
  42. [42] D. Borzelli, S. Pastorelli, and L. Gastaldi, “Model of the human arm stiffness exerted by two antagonist muscles,” 25th Int. Conf. on Robotics in Alpe-Adria-Danube Region, November 2016.
  43. [43] D. Borzelli, S. Pastorelli, and L. Gastaldi, “Determination of the Human Arm Stiffness Efficiency with a Two Antagonist Muscles Model,” in Mechanisms and Machine Science, pp. 71-78, November 2017.
  44. [44] D. A. Kistemaker, J. D. Wong, and P. L. Gribble, “The central nervous system does not minimize energy cost in arm movements,” J. Neurophysiol., Vol.104, pp. 2985-2994, December 2010.
  45. [45] K. R. S. Holzbaur, W. M. Murray, and S. L. Delp, “A Model of the Upper Extremity for Simulating Musculoskeletal Surgery and Analyzing Neuromuscular Control,” Ann. Biomed. Eng., Vol.33, pp. 829-840, June 2005.
  46. [46] J. P. Iannotti and R. D. Parker, “The Netter Collections of Medical Illustrations: Musculoskeletal System, part I – Upper Limb,” 2nd ed., Vol.6. Philadelphia, PA: Saunders, 2013.
  47. [47] J. M. Inouye and F. J. Valero-Cuevas, “Muscle Synergies Heavily Influence the Neural Control of Arm Endpoint Stiffness and Energy Consumption,” PLoS Comput. Biol., Vol.12, p. e1004737, February 2016.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 19, 2024