Skip to main content
SpringerLink
Log in

Comparison of muscle synergies extracted from both legs during cycling at different mechanical conditions

  • Technical Paper
  • Published:
Australasian Physical & Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

Muscle synergies are the building blocks for generating movement by the central nervous system (CNS). According to this hypothesis, CNS decreases the complexity of motor control by combination of a small number of muscle synergies. The aim of this work is to investigate similarity of muscle synergies during cycling across various mechanical conditions. Twenty healthy subjects performed three 6- min cycling tasks at over a range of rotational speed (40, 50, and 60 rpm) and resistant torque (3, 5, and 7 N/m). Surface electromyography (sEMG) signals were recorded during pedaling from eight muscles of the right and left legs. We extracted four synchronous muscle synergies by using the non-negative matrix factorization (NMF) method. Mean and standard deviation of the goodness of the signal reconstruction (R2) for all subjects was obtained 0.9898 ± 0.0535. We investigated the functional roles of both leg muscles during cycling by synchronous muscle synergy extraction. We compared the muscle synergies extracted from all subjects in all mechanical conditions. The total mean and standard deviation of the similarity of synergy vectors for all subjects in all mechanical conditions was obtained 0.8788 ± 0.0709. We found the high degrees of similarity among the sets of synchronous muscle synergies across mechanical conditions and also across different subjects. Our results demonstrated that different subjects at different mechanical conditions use the same motor control strategies for cycling, despite inter-individual variability of muscle patterns.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Bernstein NA (1967) The co-ordination and regulation of movements. Pergamon Press, London

    Google Scholar 

  2. Latash ML, Gorniak S, Zatsiorsky VM (2008) Hierarchies of synergies in human movements. Kinesiology 40:29–38

    PubMed  Google Scholar 

  3. Hug F, Turpin NA, Couturier A, Dorel S (2011) Consistency of muscle synergies during pedaling across different mechanical constraints. J Neurophysiol 106(1):91–103

    Article  PubMed  Google Scholar 

  4. Ting LH, Chvatal SA (2010) Decomposing muscle activity in motor tasks. In: Danion F, Latash ML (eds) Motor control theories, experiments and applications. Oxford University Press, New York, pp 102–138

    Chapter  Google Scholar 

  5. De Rugy A, Loeb GE, Carroll TJ (2013) Are muscle synergies useful for neural control? Front Comput Neurosci 7:19

    PubMed  PubMed Central  Google Scholar 

  6. d’Avella A, Giese M, Ivanenko YP, Schack T, Flash T (2015) Modularity in motor control: from muscle synergies to cognitive action representation. Front Comput Neurosci 9:126

    PubMed  PubMed Central  Google Scholar 

  7. Latash ML (2010) Stages in learning motor synergies: a view based on the equilibrium-point hypothesis. Hum Mov Sci 29(5):642–654

    Article  PubMed  PubMed Central  Google Scholar 

  8. Oliveira AS, Gizzi L, Farina D, Kersting UG (2014) Motor modules of human locomotion: influence of EMG averaging, concatenation, and number of step cycles. Front Hum Neurosci 8:335

    Article  PubMed  PubMed Central  Google Scholar 

  9. Costa-García A, Itkonen M, Yamasaki H, Shibata-Alnajjar F, Shimoda S (2018) A novel approach to the segmentation of sEMG data based on the activation and deactivation of muscle synergies during movement. IEEE Robot Autom Lett 3(3):1972–1977

    Article  Google Scholar 

  10. d’Avella A, Lacquaniti F (2013) Control of reaching movements by muscle synergy combinations. Front Comput Neurosci 7:42

    PubMed  PubMed Central  Google Scholar 

  11. Coscia M, Cheung VC, Tropea P, Koenig A, Monaco V, Bennis C, Micera S, Bonato P (2014) The effect of arm weight support on upper limb muscle synergies during reaching movements. J Neuroeng Rehabil 11(1):22

    Article  PubMed  PubMed Central  Google Scholar 

  12. Scano A, Chiavenna A, Malosio M, Molinari Tosatti L, Molteni F (2017) Muscle synergies-based characterization and clustering of poststroke patients in reaching movements. Front Bioeng Biotechnol 5:62

    Article  PubMed  PubMed Central  Google Scholar 

  13. D’Andola M, Cesqui B, Fernandez L, D’Avella A (2013) Spatiotemporal characteristics of muscle patterns for ball catching. Front Comput Neurosci 7:107

    PubMed  PubMed Central  Google Scholar 

  14. Chvatal SA, Ting LH (2013) Common muscle synergies for balance and walking. Front Comput Neurosci 7:48

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kim Y, Bulea TC, Damiano DL (2016) Novel methods to enhance precision and reliability in muscle synergy identification during walking. Front Hum Neurosci 10:455

    PubMed  PubMed Central  Google Scholar 

  16. Rimini D, Agostini V, Knaflitz M (2017) Evaluation of muscle synergies stability in human locomotion: a comparison between normal and fast walking speed. In: IEEE international instrumentation and measurement technology conference (I2MTC), Turin, Italy, IEEE. https://doi.org/10.1109/I2MTC.2017.7969722

  17. Jacobs DA, Koller JR, Steele KM, Ferris DP (2018) Motor modules during adaptation to walking in a powered ankle exoskeleton. J Neuroeng Rehabil 15(1):2

    Article  PubMed  PubMed Central  Google Scholar 

  18. Matsunaga N, Imai A, Kaneoka K (2017) Comparison of muscle synergies before and after 10 minutes of running. J Phys Ther Sci 29(7):1242–1246

    Article  PubMed  PubMed Central  Google Scholar 

  19. Nishida K, Hagio S, Kibushi B, Moritani T, Kouzaki M (2017) Comparison of muscle synergies for running between different foot strike patterns. PLoS ONE 12(2):e0171535

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. An Q, Yamakawa H, Yamashita A, Asama H (2017) Different temporal structure of muscle synergy between sit-to-walk and sit-to-stand motions in human standing leg. Converging clinical and engineering research on neurorehabilitation II. Biosys Biorobot 15:933–937. https://doi.org/10.1007/978-3-319-46669-9_151

    Article  Google Scholar 

  21. Yang N, An Q, Yamakawa H, Tamura Y, Yamashita A, Asama H (2017) Clarification of muscle synergy structure during standing-up motion of healthy young, elderly and post-stroke patients. In: IEEE international conference on rehabilitation robotics (ICORR), London, UK, IEEE, pp 19–24. https://doi.org/10.1109/ICORR.2017.8009215

  22. Oliveira AS, Gizzi L, Ketabi S, Farina D, Kersting UG (2016) Modular control of treadmill vs overground running. PLoS ONE 11(4):e0153307

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Saito A, Tomita A, Ando R, Watanabe K, Akima H (2018) Similarity of muscle synergies extracted from the lower limb including the deep muscles between level and uphill treadmill walking. Gait Posture 59:134–139

    Article  PubMed  Google Scholar 

  24. Kibushi B, Hagio S, Moritani T, Kouzaki M (2018) Speed-dependent modulation of muscle activity based on muscle synergies during treadmill walking. Front Hum Neurosci 12:4

    Article  PubMed  PubMed Central  Google Scholar 

  25. De Marchis C, Castronovo AM, Bibbo D, Schmid M, Conforto S (2012) Muscle synergies are consistent when pedaling under different biomechanical demands. In: 34th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBS), San Diego, California, USA, pp 3308–3311. https://doi.org/10.1109/EMBC.2012.6346672

  26. Saito A, Watanabe K, Akima H (2015) Coordination among thigh muscles including the vastus intermedius and adductor magnus at different cycling intensities. Hum Mov Sci 40:14–23

    Article  PubMed  Google Scholar 

  27. Torricelli D, Tobaruela DN, De Marchis C, Barroso F, Pons JL (2016) Is modular control of cycling affected by learning? Preliminary results using muscle biofeedback. In: Converging clinical and engineering research on neurorehabilitation II;Biosys Biorobot 15:919–923

  28. Hug F, Turpin NA, Guével A, Dorel S (2010) Is interindividual variability of EMG patterns in trained cyclists related to different muscle synergies? J Appl Physiol 108(6):1727–1736

    Article  PubMed  Google Scholar 

  29. Barroso FO, Torricelli D, Bravo-Esteban E, Taylor J, Gómez-Soriano J, Santos C, Moreno JC, Pons JL (2015) Muscle synergies in cycling after incomplete spinal cord injury: correlation with clinical measures of motor function and spasticity. Front Hum Neurosci 9:706

    PubMed  Google Scholar 

  30. Turpin NA, Costes A, Moretto P, Watier B (2017) Can muscle coordination explain the advantage of using the standing position during intense cycling? J Sci Med Sport 20(6):611–616

    Article  PubMed  Google Scholar 

  31. De Marchis C, Schmid M, Bibbo D, Bernabucci I, Conforto S (2013) Inter-individual variability of forces and modular muscle coordination in cycling: a study on untrained subjects. Hum Mov Sci 32(6):1480–1494

    Article  PubMed  Google Scholar 

  32. De Marchis C, Severini G, Castronovo AM, Schmid M, Conforto S (2015) Intermuscular coherence contributions in synergistic muscles during pedaling. Exp Brain Res 233(6):1907–1919

    Article  PubMed  Google Scholar 

  33. Hug F, Dorel S (2009) Electromyographic analysis of pedaling: a review. J Electromyogr Kinesiol 19(2):182–198

    Article  PubMed  Google Scholar 

  34. Momeni K, Faghri PD, Evans M (2014) Lower-extremity joint kinematics and muscle activations during semi-reclined cycling at different workloads in healthy individuals. J Neuroeng Rehabil 11(1):146

    Article  PubMed  PubMed Central  Google Scholar 

  35. Tang L, Li F, Cao S, Zhang X, Wu D, Chen X (2015) Muscle synergy analysis in children with cerebral palsy. J Neural Eng 12(4):046017

    Article  PubMed  Google Scholar 

  36. Blake OM, Champoux Y, Wakeling JM (2012) Muscle coordination patterns for efficient cycling. Med Sci Sports Exerc 44(5):926–938

    Article  PubMed  Google Scholar 

  37. So RC, Ng JKF, Ng GY (2005) Muscle recruitment pattern in cycling: a review. Phys Ther Sport 6(2):89–96

    Article  Google Scholar 

  38. Hug F (2011) Can muscle coordination be precisely studied by surface electromyography? J Electromyogr Kinesiol 21(1):1–12

    Article  PubMed  Google Scholar 

  39. McKay JL, Ting LH (2007) Functional muscle synergies constrain force production during postural tasks. J Biomech 41(2):299–306

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wojtara T, Alnajjar F, Shimoda S, Kimura H (2014) Muscle synergy stability and human balance maintenance. J Neuroeng Rehabil 11(1):129

    Article  PubMed  PubMed Central  Google Scholar 

  41. Li Y, Ngom A (2013) The non-negative matrix factorization toolbox for biological data mining. Source Code Biol Med 8(1):10

    Article  PubMed  PubMed Central  Google Scholar 

  42. Tresch MC, Cheung VC, d’Avella A (2006) Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. J Neurophysiol 95(4):2199–2212

    Article  PubMed  Google Scholar 

  43. Cheung VC, Piron L, Agostini M, Silvoni S, Turolla A, Bizzi E (2009) Stability of muscle synergies for voluntary actions after cortical stroke in humans. Proc Natl Acad Sci USA 106(46):19563–19568

    Article  CAS  PubMed  Google Scholar 

  44. Berry MW, Browne M, Langville AN, Pauca VP, Plemmons RJ (2007) Algorithms and applications for approximate nonnegative matrix factorization. Comput Stat Data An 52(1):155–173

    Article  Google Scholar 

  45. Choi C, Kim J (2011) Synergy matrices to estimate fluid wrist movements by surface electromyography. Med Eng Phys 33(8):916–923

    Article  PubMed  Google Scholar 

  46. Gentner R, Edmunds T, Pai DK, d’Avella A (2013) Robustness of muscle synergies during visuomotor adaptation. Front Comput Neurosci 7:120

    Article  PubMed  PubMed Central  Google Scholar 

  47. Delis I, Berret B, Pozzo T, Panzeri S (2013) A methodology for assessing the effect of correlations among muscle synergy activations on task-discriminating information. Front Comput Neurosci 7:54

    PubMed  PubMed Central  Google Scholar 

  48. Chiovetto E, Huber ME, Sternad D, Giese MA (2018) Low-dimensional organization of angular momentum during walking on a narrow beam. Sci Rep 8(1):95

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Steele KM, Tresch MC, Perreault EJ (2013) The number and choice of muscles impact the results of muscle synergy analyses. Front Comput Neurosci 7:105

    Article  PubMed  PubMed Central  Google Scholar 

  50. d’Avella A, Portone A, Lacquaniti F (2011) Superposition and modulation of muscle synergies for reaching in response to a change in target location. J Neurophysiol 106(6):2796–2812

    Article  PubMed  Google Scholar 

  51. d’Avella A, Portone A, Fernandez L, Lacquaniti F (2006) Control of fast-reaching movements by muscle synergy combinations. J Neurosci 26(30):7791–7810

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Quandt F, Hummel FC (2014) The influence of functional electrical stimulation on hand motor recovery in stroke patients: a review. Exp Transl Stroke Med 6(1):9

    Article  PubMed  PubMed Central  Google Scholar 

  53. Toledo-Peral CL, Gutiérrez-Martínez J, Mercado-Gutiérrez JA, Martín-Vignon-Whaley AI, Vera-Hernández A, Leija-Salas L (2018) sEMG signal acquisition strategy towards Hand FES Control. J Healthc Eng. https://doi.org/10.1155/2018/2350834

  54. Alessandro C, Delis I, Nori F, Panzeri S, Berret B (2013) Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives. Front Comput Neurosci 7:43

    Article  PubMed  PubMed Central  Google Scholar 

  55. Ison M, Artemiadis P (2015) Proportional myoelectric control of robots: muscle synergy development drives performance enhancement, retainment, and generalization. IEEE Trans Robot 31(2):259–268

    Article  Google Scholar 

  56. Taborri J, Agostini V, Artemiadis PK, Ghislieri M, Jacobs DA, Roh J, Rossi S (2018) Feasibility of muscle synergy outcomes in clinics, robotics, and sports: a systematic review. Appl Bionics Biomech. https://doi.org/10.1155/2018/3934698

Download references

Author information

Authors and Affiliations

  1. Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran

    Javad Esmaeili

  2. Biomedical Engineering Department, Semnan University, Semnan, Iran

    Ali Maleki

Authors
  1. Javad Esmaeili
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Maleki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Maleki.

Ethics declarations

Conflict of interest

The authors declare that there are no conflicts of interest with this paper.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Esmaeili, J., Maleki, A. Comparison of muscle synergies extracted from both legs during cycling at different mechanical conditions. Australas Phys Eng Sci Med 42, 827–838 (2019). https://doi.org/10.1007/s13246-019-00767-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-019-00767-0

Keywords

Search

Navigation