Motor Control Based on the Muscle Synergy Hypothesis

  • Hiroaki Hirai
  • Hang Pham
  • Yohei Ariga
  • Kanna Uno
  • Fumio Miyazaki


In neuroscience, the idea that motor behaviors are constructed by a combination of building blocks has been supported by a large amount of experimental evidences. The idea has been very attractive as a powerful strategy for solving the motor redundancy problem. While there are some candidates for motor primitives, such as submovements, oscillations, and mechanical impedances, synergies are one of the candidates for motor modules or composite units for motor control. Synergies are usually extracted by applying statistic techniques to explanatory variables, such as joint angles and electromyography (EMG) signals, and by decomposing these variables into fewer units. The results of factor decomposition are, however, not necessarily interpretable with these explanatory variables, even though the factors successfully reduce the dimensionality of movement; therefore, the physical meaning of synergies is unclear in most cases. To obtain insight into the meaning of synergies, this chapter proposes the agonist-antagonist muscle-pair (A-A) concept and uses other explanatory variables: the A-A ratio, which is related to the equilibrium point (EP), and the A-A sum, which is associated with mechanical stiffness. The A-A concept can be regarded as a form comparable to the EP hypothesis (EPH, λ model), and it can be extended to the novel concept of EP-based synergies. These explanatory variables enable us to identify muscle synergies from human EMG signals and to interpret the physical meaning of the extracted muscle synergies. This chapter introduces the EMG analysis in hand-force generation of a human upper limb and shows that the endpoint (hand) movement is governed by two muscle synergies for (1) radial movement generation and (2) angular movement generation in a polar coordinate system centered on the shoulder joint. On the basis of the analysis, a synergy-based framework of human motor control is hypothesized, and it can explain the mechanism of the movement control in a simple way.


Human motor control Muscle synergy Equilibrium point (EP) Joint stiffness Agonist-antagonist muscle-pair (A-A) concept A-A ratio A-A sum Pneumatic artificial muscle (PAM) Electromyography (EMG) Principal component analysis (PCA) Hand force 



The second half of this chapter is modified from the original (Pham et al. 2014), with kind permission from Taylor & Francis.


  1. Ariga, Y., Pham, H., Uemura, M., Hirai, H., Miyazaki, F.: Novel equilibrium-point control of agonist-antagonist system with pneumatic artificial muscles. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA2012), Saint Paul, pp. 1470–1475 (2012a)Google Scholar
  2. Ariga, Y., Maeda, D., Pham, H., Uemura, M., Hirai, H., Miyazaki, F.: Novel equilibrium-point control of agonist-antagonist system with pneumatic artificial muscles: II. Application to EMG-based human-machine interface for an elbow-joint system. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS2012), Vilamoura-Algarve, pp. 4380–4385 (2012b)Google Scholar
  3. Artemiadis, P.K., Kyriakopoulos, K.J.: EMG-based control of a robot arm using low-dimensional embeddings. IEEE Trans. Robot. 26(2), 393–398 (2010)CrossRefGoogle Scholar
  4. Bernstein, N.: The Coordination and Regulation of Movements. Pergamon Press, Oxford (1967)Google Scholar
  5. Bizzi, E., d’Avella, A., Saltiel, P., Tresch, M.: Modular organization of spinal motor systems. Neuroscientist 8(5), 437–422 (2002)CrossRefGoogle Scholar
  6. Bizzi, E., Cheung, V.C.K., d’Avella, A., Saltiel, P., Tresch, M.: Combining modules for movement. Brain Res. Rev. 57(1), 125–133 (2008)CrossRefGoogle Scholar
  7. Chou, C., Hannaford, B.: Measurement and modeling of Mckibben pneumatic artificial muscle. IEEE Trans. Robot. Autom. 12(1), 90–102 (1996)CrossRefGoogle Scholar
  8. d’Avella, A., Bizzi, E.: Shared and specific muscle synergies in natural motor behaviours. Proc. Natl. Acad. Sci. USA 102(8), 3076–3081 (2005)CrossRefGoogle Scholar
  9. d’Avella, A., Saltiel, P., Bizzi, E.: Combinations of muscle synergies in the construction of a natural motor behavior. Nat. Neurosci. 6(3), 300–308 (2003)CrossRefGoogle Scholar
  10. Feldman, A.G.: Functional tuning of the nervous system with control of movement or maintenance of a steady posture, II: controllable parameters of the muscle. Biophysics 11, 565–578 (1966)Google Scholar
  11. Feldman, A.G., Levin, M.F.: The equilibrium-point hypothesis – past, present and future. In: Progress in Motor Control, A Multidisciplinary Perspective, pp. 699–726. Springer, Dordrecht (2008)Google Scholar
  12. Feldman, A.G., Adamovich, S.V., Ostry, D.J., Flanagan, J.R.: The origin of electromyograms – explanations based on the equilibrium-point hypothesis. In: Multiple Muscle Systems, Biomechanics and Movement Organization, pp. 195–213. Springer, New York (1990)Google Scholar
  13. Fujimoto, S., Ono, T., Ohsaka, K., Zhao, Z.: Modeling of artificial muscle actuator and control design for antagonistic drive system. Trans. Jpn. Soc. Mech. Eng. 73(730), 1777–1785 (2007) (in Japanese)CrossRefGoogle Scholar
  14. Giszter, S., Patil, V., Hart, C.: Primitives, premotor drives, and pattern generation: a combined computational and neuroethological perspective. Prog. Brain Res. 165, 323–346 (2007)CrossRefGoogle Scholar
  15. Gottlieb, G.L.: Muscle activation patterns during two types of voluntary single-joint movement. J. Neurophysiol. 80(4), 1860–1867 (1998)MathSciNetGoogle Scholar
  16. Hislop, H., Montgomery, J.: Daniels Worthingham’s Muscle Testing: Techniques of Manual Examination, 8th edn. Saunders, St. Louis (2007)Google Scholar
  17. Ivanenko, Y.P., Popplele, R.E., Lacquaniti, F.: Five basic muscle activation patterns account for muscle activity during human locomotion. J. Physiol. 556(1), 267–282 (2004)CrossRefGoogle Scholar
  18. Jolliffe, I.: Principal Components Analysis. Springer, New York (1986)CrossRefMATHGoogle Scholar
  19. Kagawa, T., Fujita, T., Yamanaka, T.: Nonlinear model of artificial muscle. Trans. Soc. Inst. Control Eng. 29(10), 1241–1243 (1993) (in Japanese)Google Scholar
  20. Latash, M.L.: Evolution of motor control: from reflexes and motor programs to the equilibrium-point hypothesis. J. Hum. Kinet. 19(19), 3–24 (2008a)Google Scholar
  21. Latash, M.L.: Synergy. Oxford University Press, Oxford/New York (2008b)CrossRefGoogle Scholar
  22. Milner, T.E., Cloutier, C., Leger, A.B., Franklin, D.W.: Inability to activate muscles maximally during cocontraction and the effect on joint stiffness. Exp. Brain Res. 107(2), 293–305 (1995)CrossRefGoogle Scholar
  23. Pham, H., Kimura, M., Hirai, H., Miyazaki, F.: Extraction and implementation of muscle synergies in hand-force control. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA2011), Shanghai, pp. 3658–3663 (2011)Google Scholar
  24. Pham, H., Ariga, Y., Tominaga, K., Oku, T., Nakayama, K., Uemura, M., Hirai, H., Miyazaki, F.: Extraction and implementation of muscle synergies in neuro-mechanical control of upper limb movement. Adv. Robot. 28(11), 745–757 (2014)Google Scholar
  25. Shin, D., Kim, J., Koike, Y.: A myokinetic arm model for estimating joint torque and stiffness from EMG signals during maintained posture. J. Neurophysiol. 101(1), 387–401 (2009)CrossRefGoogle Scholar
  26. Ting, L.H.: Dimensional reduction in sensorimotor systems: a framework for understanding muscle coordination of posture. Prog. Brain Res. 165, 299–321 (2007)CrossRefGoogle Scholar
  27. Tondu, B., Lopez, P.: Modeling and control of McKibben artificial muscle robot actuators. IEEE Control Syst. Mag. 20(2), 15–38 (2000)CrossRefGoogle Scholar

Copyright information

© Springer Japan 2016

Authors and Affiliations

  • Hiroaki Hirai
    • 1
  • Hang Pham
    • 1
  • Yohei Ariga
    • 1
  • Kanna Uno
    • 1
  • Fumio Miyazaki
    • 1
  1. 1.Graduate School of Engineering ScienceOsaka UniversityToyonakaJapan

Personalised recommendations