Advertisement

Muscle Synergy Analysis for Stand-Squat and Squat-Stand Tasks with sEMG Signals

  • Chao Chen
  • Farong Gao
  • Chunling Sun
  • Qiuxuan Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

Abstract

Human walking is the composite movement of the musculoskeletal system in lower limbs. The interaction mechanism of the different muscle groups in a combination action is of great importance. To this end, under the stand-squat and squat-stand tasks, the problems of the motion model decomposition and the muscle synergy were studied in this paper. Firstly, the envelopes were extracted from acquired and de-noised surface electromyography (sEMG) signals. Secondly, the non-negative matrix factorization (NMF) algorithm was explored to decompose the four synergistic modules and the corresponding activation coefficients under the two tasks. Finally, the relationship between the muscle synergy and the lower limb movement was discussed in normal and fatigue subjects. The results show that muscle participation of each synergistic module is consistent with the physiological function, and exhibit some differences in muscle synergies between normal and fatigue states. This work can help to understand the control strategies of the nervous system in lower extremity motor and have some significance for the evaluation of limb rehabilitation.

Keywords

Lower extremity motor sEMG signal Muscle synergy Envelope NMF algorithm Fatigue state 

References

  1. 1.
    Berchtold, M.W., Brinkmeier, H., Müntener, M.: Calcium ion in skeletal muscle: its crucial role for muscle function, plasticity, and disease. Physiol. Rev. 80, 1215–1225 (2000)CrossRefGoogle Scholar
  2. 2.
    Hopkins, P.M.: Skeletal muscle physiology. Continuing Educ. Anaesth. Crit. Care Pain 6, 1–6 (2006)CrossRefGoogle Scholar
  3. 3.
    Pan, L., Zhang, D., Liu, J., Sheng, X., Zhu, X.: Continuous estimation of finger joint angles under different static wrist motions from surface EMG signals. Biomed. Sig. Process. Control 14, 265–271 (2014)CrossRefGoogle Scholar
  4. 4.
    Berger, D.J., D’Avella, A.: Effective force control by muscle synergies. Front. Comput. Neurosci. 8, 46–57 (2014)CrossRefGoogle Scholar
  5. 5.
    Gao, F.R., Wang, J.J., Xi, X.G., She, Q.S., Luo, Z.Z.: Gait recognition for lower extremity ElectroMyoGraphic signals based on PSO-SVM method. J. Electron. Inf. Technol. 37, 1154–1159 (2015)Google Scholar
  6. 6.
    Wang, J.J., Gao, F.R., Sun, Y., Luo, Z.Z.: Non-uniform characteristics and its recognition effects for walking gait based on sEMG. Chin. J. Sens. Actuators 29, 384–389 (2016)Google Scholar
  7. 7.
    Li, Y., Gao, F.R., Chen, H.H., Xu, M.H.: Gait recognition based on EMG with different individuals and sample sizes. In: 35th Chinese Control Conference (CCC) on Proceedings, pp. 4068–4072 (2016)Google Scholar
  8. 8.
    Julien, F., François, H.: Between-subject variability of muscle synergies during a complex motor skill. Front. Comput. Neurosci. 6, 49–58 (2012)Google Scholar
  9. 9.
    Chen, X., Niu, X., Wu, D., Yu, Y., Zhang, X.: Investigation of the intra- and inter-limb muscle coordination of hands-and-knees crawling in human adults by means of muscle synergy analysis. Entropy 19, 229 (2017)CrossRefGoogle Scholar
  10. 10.
    Chia, B.N., et al.: Tuning of muscle synergies during walking along rectilinear and curvilinear trajectories in humans. Ann. Biomed. Eng. 45, 1–15 (2017)CrossRefGoogle Scholar
  11. 11.
    Gizzi, L., Muceli, S., Petzke, F., Falla, D.: Experimental muscle pain impairs the synergistic modular control of neck muscles. PLoS ONE 10, 399–412 (2015)CrossRefGoogle Scholar
  12. 12.
    Yang, S., Mao, Y.: Global minima analysis of Lee and Seung’s NMF algorithms. Neural Process. Lett. 38, 29–51 (2013)CrossRefGoogle Scholar
  13. 13.
    D’Alessio, T., Conforto, S.: Extraction of the envelope from surface EMG signals. IEEE Eng. Med. Biol. Mag. Q. Mag. Eng. Med. Biol. Soc. 20, 55–83 (2001)CrossRefGoogle Scholar
  14. 14.
    Steele, K.M., Tresch, M.C., Perreault, E.J.: The number and choice of muscles impact the results of muscle synergy analyses. Front. Comput. Neurosci. 7, 105–114 (2013)CrossRefGoogle Scholar
  15. 15.
    Clark, D.J., Ting, L.H., Zajac, F.E., Neptune, R.R., Kautz, S.A.: Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke. J. Neurophysiol. 103, 844 (2010)CrossRefGoogle Scholar
  16. 16.
    Stein, R.B., et al.: Coding of position by simultaneously recorded sensory neurones in the cat dorsal root ganglion. J. Physiol. 560, 883–896 (2004)CrossRefGoogle Scholar
  17. 17.
    Miranda, E.F., Malaguti, C., Marchetti, P.H., Dal, C.S.: Upper and lower limb muscles in patients with COPD: similarities in muscle efficiency but differences in fatigue resistance. Respir. Care 59, 62–69 (2013)CrossRefGoogle Scholar
  18. 18.
    Cifrek, M., Medved, V., Tonković, S., Ostojić, S.: Surface EMG based muscle fatigue evaluation in biomechanics. Clin. Biomech. 24, 327–340 (2009)CrossRefGoogle Scholar
  19. 19.
    Thaler, L., Goodale, M.A.: Neural substrates of visual spatial coding and visual feedback control for hand movements in allocentric and target-directed tasks. Front. Hum. Neurosci. 5, 92–115 (2011)CrossRefGoogle Scholar
  20. 20.
    Tsuji, T., Shima, K., Murakami, Y.: Pattern classification of combined motions based on muscle synergy theory. J. Rob. Soc. Jpn. 28, 606–613 (2010)CrossRefGoogle Scholar
  21. 21.
    Danuta, R.L.: The influence of confounding factors on the relationship between muscle contraction level and MF and MPF values of EMG signal: a review. Int. J. Occup. Saf. Ergon. 22, 77–91 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Chao Chen
    • 1
  • Farong Gao
    • 1
  • Chunling Sun
    • 1
  • Qiuxuan Wu
    • 1
  1. 1.School of AutomationHangzhou Dianzi UniversityHangzhouChina

Personalised recommendations