Skip to main content

Motor Control Based on the Muscle Synergy Hypothesis

  • Chapter
Cognitive Neuroscience Robotics A

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • 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 

  • 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 

  • 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)

    Article  Google Scholar 

  • Bernstein, N.: The Coordination and Regulation of Movements. Pergamon Press, Oxford (1967)

    Google Scholar 

  • Bizzi, E., d’Avella, A., Saltiel, P., Tresch, M.: Modular organization of spinal motor systems. Neuroscientist 8(5), 437–422 (2002)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Chou, C., Hannaford, B.: Measurement and modeling of Mckibben pneumatic artificial muscle. IEEE Trans. Robot. Autom. 12(1), 90–102 (1996)

    Article  Google Scholar 

  • d’Avella, A., Bizzi, E.: Shared and specific muscle synergies in natural motor behaviours. Proc. Natl. Acad. Sci. USA 102(8), 3076–3081 (2005)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Gottlieb, G.L.: Muscle activation patterns during two types of voluntary single-joint movement. J. Neurophysiol. 80(4), 1860–1867 (1998)

    MathSciNet  Google Scholar 

  • Hislop, H., Montgomery, J.: Daniels Worthingham’s Muscle Testing: Techniques of Manual Examination, 8th edn. Saunders, St. Louis (2007)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • Jolliffe, I.: Principal Components Analysis. Springer, New York (1986)

    Book  MATH  Google Scholar 

  • 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 

  • 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 

  • Latash, M.L.: Synergy. Oxford University Press, Oxford/New York (2008b)

    Book  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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 

  • 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 

  • 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)

    Article  Google Scholar 

  • Ting, L.H.: Dimensional reduction in sensorimotor systems: a framework for understanding muscle coordination of posture. Prog. Brain Res. 165, 299–321 (2007)

    Article  Google Scholar 

  • Tondu, B., Lopez, P.: Modeling and control of McKibben artificial muscle robot actuators. IEEE Control Syst. Mag. 20(2), 15–38 (2000)

    Article  Google Scholar 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hiroaki Hirai .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Japan

About this chapter

Cite this chapter

Hirai, H., Pham, H., Ariga, Y., Uno, K., Miyazaki, F. (2016). Motor Control Based on the Muscle Synergy Hypothesis. In: Kasaki, M., Ishiguro, H., Asada, M., Osaka, M., Fujikado, T. (eds) Cognitive Neuroscience Robotics A. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54595-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-4-431-54595-8_2

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-54594-1

  • Online ISBN: 978-4-431-54595-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics