Display options
Cite
Sharif Razavian R, Mehrabi N, McPhee J. A model-based approach to predict muscle synergies using optimization: application to feedback control. Front Comput Neurosci. 2015;9:121doi: 10.3389/fncom.2015.00121.
Sharif Razavian, R., Mehrabi, N., & McPhee, J. (2015). A model-based approach to predict muscle synergies using optimization: application to feedback control. Frontiers in computational neuroscience, 9121. https://doi.org/10.3389/fncom.2015.00121
Sharif Razavian, Reza, et al. "A model-based approach to predict muscle synergies using optimization: application to feedback control." Frontiers in computational neuroscience vol. 9 (2015): 121. doi: https://doi.org/10.3389/fncom.2015.00121
Sharif Razavian R, Mehrabi N, McPhee J. A model-based approach to predict muscle synergies using optimization: application to feedback control. Front Comput Neurosci. 2015 Oct 06;9:121. doi: 10.3389/fncom.2015.00121. eCollection 2015. PMID: 26500530; PMCID: PMC4593861.
Copy Download .nbib Format: NLM
Share it on

Front Comput Neurosci. 2015 Oct 06;9:121. doi: 10.3389/fncom.2015.00121. eCollection 2015.

A model-based approach to predict muscle synergies using optimization: application to feedback control.

Frontiers in computational neuroscience

Reza Sharif Razavian, Naser Mehrabi, John McPhee

Affiliations

  1. Department of Systems Design Engineering, University of Waterloo Waterloo, ON, Canada.

PMID: 26500530 PMCID: PMC4593861 DOI: 10.3389/fncom.2015.00121

Abstract

This paper presents a new model-based method to define muscle synergies. Unlike the conventional factorization approach, which extracts synergies from electromyographic data, the proposed method employs a biomechanical model and formally defines the synergies as the solution of an optimal control problem. As a result, the number of required synergies is directly related to the dimensions of the operational space. The estimated synergies are posture-dependent, which correlate well with the results of standard factorization methods. Two examples are used to showcase this method: a two-dimensional forearm model, and a three-dimensional driver arm model. It has been shown here that the synergies need to be task-specific (i.e., they are defined for the specific operational spaces: the elbow angle and the steering wheel angle in the two systems). This functional definition of synergies results in a low-dimensional control space, in which every force in the operational space is accurately created by a unique combination of synergies. As such, there is no need for extra criteria (e.g., minimizing effort) in the process of motion control. This approach is motivated by the need for fast and bio-plausible feedback control of musculoskeletal systems, and can have important implications in engineering, motor control, and biomechanics.

Keywords: dynamic redundancy; model-based approach; muscle synergy; operational space; optimization; real-time control; task-specific; unique solution

References

  1. Front Comput Neurosci. 2013 Jun 26;7:79 - PubMed
  2. J Neurophysiol. 2014 Apr;111(8):1686-702 - PubMed
  3. Nat Neurosci. 2002 Nov;5(11):1226-35 - PubMed
  4. Front Comput Neurosci. 2013 Mar 21;7:19 - PubMed
  5. J Neurosci. 2012 May 23;32(21):7384-91 - PubMed
  6. J Neurophysiol. 2009 Jul;102(1):59-68 - PubMed
  7. PLoS Comput Biol. 2012;8(5):e1002434 - PubMed
  8. Exp Brain Res. 1999 Jun;126(3):289-306 - PubMed
  9. Comput Methods Biomech Biomed Engin. 2001 Feb;4(2):93-126 - PubMed
  10. J Biomech. 2012 Aug 9;45(12):2157-63 - PubMed
  11. J Biomech. 2009 Jun 19;42(9):1282-7 - PubMed
  12. Neuroscientist. 2006 Aug;12(4):339-48 - PubMed
  13. J Robot Syst. 2005 Nov;22(11):691-710 - PubMed
  14. J Biomech Eng. 2003 Feb;125(1):70-7 - PubMed
  15. J Biomech. 2001 Feb;34(2):153-61 - PubMed
  16. Ergonomics. 1975 Nov;18(6):643-9 - PubMed
  17. J Neurophysiol. 2008 Nov;100(5):2455-71 - PubMed
  18. J Neurophysiol. 2010 Jan;103(1):573-90 - PubMed
  19. Front Comput Neurosci. 2013 Aug 08;7:105 - PubMed
  20. Curr Opin Neurobiol. 2009 Dec;19(6):601-7 - PubMed
  21. J Neurophysiol. 2008 Sep;100(3):1433-54 - PubMed
  22. Proc Natl Acad Sci U S A. 2009 May 5;106(18):7601-6 - PubMed
  23. J Neurophysiol. 2014 Feb;111(3):675-93 - PubMed
  24. Prog Brain Res. 2007;165:299-321 - PubMed
  25. Biol Cybern. 2012 Dec;106(11-12):757-65 - PubMed
  26. Front Comput Neurosci. 2014 Apr 17;8:46 - PubMed
  27. Exp Brain Res. 1981;42(2):223-7 - PubMed
  28. Top Spinal Cord Inj Rehabil. 2011 Summer;17(1):16-24 - PubMed
  29. Nat Neurosci. 2007 Oct;10(10):1329-36 - PubMed
  30. J Neurophysiol. 2011 Sep;106(3):1363-78 - PubMed
  31. J Biomech. 2008;41(2):299-306 - PubMed
  32. Clin Biomech (Bristol, Avon). 2007 Feb;22(2):131-54 - PubMed
  33. Brain Res Rev. 2008 Jan;57(1):125-33 - PubMed
  34. Neural Netw. 2008 May;21(4):642-53 - PubMed
  35. J Neurosci. 2010 Jul 14;30(28):9431-44 - PubMed
  36. Hum Mov Sci. 2010 Oct;29(5):684-700 - PubMed
  37. Comput Methods Biomech Biomed Engin. 2013;16(3):291-301 - PubMed
  38. Science. 1999 Sep 24;285(5436):2136-9 - PubMed

Publication Types