Analysis of muscle synergy and muscle functional network at different walking speeds based on surface electromyographic signal_Journal of Biomedical Engineering

Authors：

CUI Caihong ^1,2 , MIAO Huacong ^2,3 ,  LIANG Tie ^2,3 , LIU Xiuling ^2,3 ,  LIU Xiaoguang ^2,3

1. Department of Rehabilitation Medicine, Affiliated Hospital of Hebei University, Baoding, Hebei 071002, P. R. China;
2. Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding, Hebei 071002, P. R. China;
3. School of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P. R. China;

Corresponding author：

LIANG Tie, Email: hbuliangtie@163.com; LIU Xiaoguang, Email: lxg_hbu@163.com

Keywords：

Surface electromyography; Walking movement; Muscle synergy; Muscle functional network

DOI：

10.7507/1001-5515.202303065

Video：

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Abstract Full text Figures/Tables Video References Cited by

An in-depth understanding of the mechanism of lower extremity muscle coordination during walking is the key to improving the efficacy of gait rehabilitation in patients with neuromuscular dysfunction. This paper investigates the effect of changes in walking speed on lower extremity muscle synergy patterns and muscle functional networks. Eight healthy subjects were recruited to perform walking tasks on a treadmill at three different speeds, and the surface electromyographic signals (sEMG) of eight muscles of the right lower limb were collected synchronously. The non-negative matrix factorization (NNMF) method was used to extract muscle synergy patterns, the mutual information (MI) method was used to construct the alpha frequency band (8–13 Hz), beta frequency band (14–30 Hz) and gamma frequency band (31–60 Hz) muscle functional network, and complex network analysis methods were introduced to quantify the differences between different networks. Muscle synergy analysis extracted 5 muscle synergy patterns, and changes in walking speed did not change the number of muscle synergy, but resulted in changes in muscle weights. Muscle network analysis found that at the same speed, high-frequency bands have lower global efficiency and clustering coefficients. As walking speed increased, the strength of connections between local muscles also increased. The results show that there are different muscle synergy patterns and muscle function networks in different walking speeds. This study provides a new perspective for exploring the mechanism of muscle coordination at different walking speeds, and is expected to provide theoretical support for the evaluation of gait function in patients with neuromuscular dysfunction.

Citation： CUI Caihong, MIAO Huacong, LIANG Tie, LIU Xiuling, LIU Xiaoguang. Analysis of muscle synergy and muscle functional network at different walking speeds based on surface electromyographic signal. Journal of Biomedical Engineering, 2023, 40(5): 938-944. doi: 10.7507/1001-5515.202303065 Copy

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12.	陈玲玲, 张存, 宋晓伟, 等. 面向外骨骼机器人的肌肉功能网络构建及分析. 生物医学工程学杂志, 2019, 36(4): 565-572.
13.	Madeleine P, Xie Y, Szeto G P Y, et al. Effects of chronic neck–shoulder pain on normalized mutual information analysis of surface electromyography during functional tasks. Clin Neurophysiol, 2016, 127(9): 3110-3117.
14.	Ting L H, Macpherson J M. A limited set of muscle synergies for force control during a postural task. J Neurophysiol, 2005, 93(1): 609-613.
15.	Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 2009, 10(3): 186-198.
16.	Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 2010, 52(3): 1059-1069.
17.	Sporns O, Chialvo D R, Kaiser M, et al. Organization, development and function of complex brain networks. Trends Cogn Sci, 2004, 8(9): 418-425.

1. Lelas J L, Merriman G J, Riley P O, et al. Predicting peak kinematic and kinetic parameters from gait speed. Gait Posture, 2003, 17(2): 106-112.
2. Inzitari M, Newman A B, Yaffe K, et al. Gait speed predicts decline in attention and psychomotor speed in older adults: the health aging and body composition study. Neuroepidemiology, 2007, 29(3-4): 156-162.
3. Li Y, Lyu Y, Song R. Phase-dependent modulation of muscle activity and intermuscular coupling during walking in patients after stroke. IEEE T Neur Sys Reh, 2023, 31: 1119-1127.
4. Aseel G, Forrest S M, Holt C A, et al. Can activities of daily living contribute to EMG normalization for gait analysis?. PLoS ONE, 2017, 12(4): e0174670.
5. Peterson S M, Ferris D P. Group-level cortical and muscular connectivity during perturbations to walking and standing balance. Neuroimage, 2019, 198: 93-103.
6. 杨毅, 彭玉鑫, 郝增明, 等. 复杂人体运动冗余控制的肌肉协同理论研究进展与展望. 体育科学, 2020, 40(12): 63-72.
7. d'Avella A, Saltiel P, Bizzi E. Combinations of muscle synergies in the construction of a natural motor behavior. Nat Neurosci, 2003, 6(3): 300-308.
8. Serrancolí G, Monllau J C, Font-Llagunes J M. Analysis of muscle synergies and activation–deactivation patterns in subjects with anterior cruciate ligament deficiency during walking. Clin Biomech, 2016, 31: 65-73.
9. Boonstra T W, Danna-Dos-Santos A, Xie H-B, et al. Muscle networks: Connectivity analysis of EMG activity during postural control. Sci Rep, 2015, 5(1): 1-14.
10. Barroso F O, Torricelli D, Moreno J C, et al. Shared muscle synergies in human walking and cycling. J Neurophysiol, 2014, 112(8): 1984-1998.
11. Liang T, Zhang Q, Liu X, et al. Time-frequency maximal information coefficient method and its application to functional corticomuscular coupling. IEEE T Neur Sys Reh, 2020, 28(11): 2515-2524.
12. 陈玲玲, 张存, 宋晓伟, 等. 面向外骨骼机器人的肌肉功能网络构建及分析. 生物医学工程学杂志, 2019, 36(4): 565-572.
13. Madeleine P, Xie Y, Szeto G P Y, et al. Effects of chronic neck–shoulder pain on normalized mutual information analysis of surface electromyography during functional tasks. Clin Neurophysiol, 2016, 127(9): 3110-3117.
14. Ting L H, Macpherson J M. A limited set of muscle synergies for force control during a postural task. J Neurophysiol, 2005, 93(1): 609-613.
15. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 2009, 10(3): 186-198.
16. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 2010, 52(3): 1059-1069.
17. Sporns O, Chialvo D R, Kaiser M, et al. Organization, development and function of complex brain networks. Trends Cogn Sci, 2004, 8(9): 418-425.

Journal of Biomedical Engineering

Analysis of muscle synergy and muscle functional network at different walking speeds based on surface electromyographic signal

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