Relationship between Muscle Synergies and Skills of Basketball Players

Document Type : Original Paper


1 Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran


Introduction: “Muscular synergy” is one of the methods for determining the relationship between the central nervous system and muscles which are involved in performing a specific movement. To perform each movement, certain patterns are followed through the central nervous system that control the number of synergies, and these patterns are modified and optimized during the skill. Thepresent study aimed to classify basketball athletes based on muscular synergy analysis.
Material and Methods: For the purpose of the study, the electromyography (EMG) signals of six dominant hand muscles were recorded during performing three basketball skills. Subsequently, synergy was identified using the non-negative matrix factorization method. In the next stage, the cosine similarity feature was calculated separately; furthermore, the time and frequency of the main signal were analyzed, and the neural network was evaluated using MATLAB software.
Results: The result of the classification was obtained by applying the dimensioned reduced matrix of all the existing features with a reliability of 73.68%. In addition, the results demonstrated that the cosine similarities between the muscles of each person could lead to the training of the neural network and classification of individuals at different levels of skill.
Conclusion: The present study suggested a new idea regarding synergistic features for classifying athletes based on EMG signal. However, the use of time and frequency features only facilitated differentiation between a maximum of two groups.


Main Subjects

  1. Lindberg M, Frisk-Kempe K, Linderhed H, Eklund J. Musculoskeletal disorders, posture and EMG temporal pattern in fabric-seaming tasks. International Journal of Industrial Ergonomics. 1993;11(3):267-76.
  2. Roman-Liu D, Bartuzi P. The influence of wrist posture on the time and frequency EMG signal measures of forearm muscles. Gait & posture. 2013;37(3):340-4.
  3. Finneran A, O'Sullivan L. Effects of grip type and wrist posture on forearm EMG activity, endurance time and movement accuracy. International Journal of Industrial Ergonomics. 2013 ;43(1):91-9.
  4. Bassement M, Garnier C, Goss-Sampson M, Watelain E, Lepoutre FX. Using EMGs and kinematics data to study the take-off technique of experts and novices for a pole vaulting short run-up educational exercise. Journal of science and medicine in sport. 2010;13(5):554-8.
  5. Frère J, Göpfert B, Slawinski J, Tourny-Chollet C. Effect of the upper limbs muscles activity on the mechanical energy gain in pole vaulting. Journal of electromyography and kinesiology. 2012;22(2):207-14.
  6. Charlton PC, Mentiplay BF, Grimaldi A, Pua YH, Clark RA. The reliability of a maximal isometric hip strength and simultaneous surface EMG screening protocol in elite, junior rugby league athletes. Journal of science and medicine in sport. 2017;20(2):139-45.
  7. Bahalgerdy F. The Combination of Reinforcement Learning and Synergy Patterns to Controlling of Human Arm Movements on the Horizontal Plane. MSc thesis of Biomedical Engineering. Islamic Azad University Science and Research Brach. 2017.
  8. Kaboodvand V. Extraction and analysis of muscle synergies in arm reaching movement. Bioengineering Department MSc Thesis. Amirkabir University of Technology (Tehran Polytechnic). 2014.
  9. Sabzevari V. Extraction of hand muscle synergies in reaching movement using fuzzy cognitive map and nonlinear quantifiers. Faculty of Biomedical Engineering-Department of Bioelectric Ph.D Thesis. Islamic Azad University Science and Research Brach, Tehran, Iran. 2015.
  10. Cole NM, Ajiboye AB. Muscle synergies for predicting non-isometric complex hand function for commanding FES neuroprosthetic hand systems. Journal of neural engineering. 2019;16(5):056018.
  11. Hu ZX, Xu SQ, Hao MZ, Xiao Q, Lan N. Muscle synergy changes with cutaneous stimulation during resting tremor and reaching task in Parkinson's disease. In2019 9th International IEEE/EMBS Conference on Neural Engineering (NER). 2019; 9: 73-76.
  12. Li T, Chen X, Cao S, Zhang X, Chen X. Human hands-and-knees crawling movement analysis based on time-varying synergy and synchronous synergy theories. Math. Biosci. Eng. 2019; 16:2492-513.
  13. Lee D, Seung S. Algorithms for non-negative matrix factorization. Adv Neural Inf Process Syst. 2001; 13:556–62.
  14. D'Avella A, Tresch MM. Modularity in the motor system: decomposition of muscle patterns as combinations of time-varying synergies. Advances in neural information processing systems. 2001; 14:141-8.
  15. Wojtara T, Alnajjar F, Shimoda S, Kimura H. Muscle synergy stability and human balance maintenance. Journal of neuroengineering and rehabilitation. 2014;11(1):129.
  16. Steele KM, Rozumalski A, Schwartz MH. Muscle synergies and complexity of neuromuscular control during gait in cerebral palsy. Developmental Medicine & Child Neurology. 2015 ;57(12):1176-82.
  17. Sabzevari VR, Jafari AH, Boostani R. Muscle synergy extraction during arm reaching movements at different speeds. Technology and Health Care. 2017;25(1):123-36.
  18. Zardoshti-Kermani M, Wheeler BC, Badie K, Hashemi RM. EMG feature evaluation for movement control of upper extremity prostheses. IEEE Transactions on Rehabilitation Engineering. 1995;3(4):324-33.
  19. Hudgins B, Parker P, Scott RN. A new strategy for multifunction myoelectric control. IEEE transactions on biomedical engineering. 1993 Jan;40(1):82-94.
  20. Oskoei MA, Hu H. Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE transactions on biomedical engineering. 2008 Mar 5;55(8):1956-65.
  21. Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert systems with applications. 2012 Jun 15;39(8):7420-31.
  22. Du S, Vuskovic M. Temporal vs. spectral approach to feature extraction from prehensile EMG signals. InProceedings of the 2004 IEEE International Conference on Information Reuse and Integration. 2004; 344-50.
  23. Rahatabad FN, Jafari AH, Fallah A, Razjouyan J. A fuzzy-genetic model for estimating forces from electromyographical activity of antagonistic muscles due to planar lower arm movements: the effect of nonlinear muscle properties. Biosystems. 2012;107(1):56-63.
  24. Rahatabad FN, Fallah A, Jafari AH. A study of chaotic phenomena in human-like reaching movements. International Journal of Bifurcation and Chaos. 2011;21(11):3293-303.
  25. Kim HK, Carmena JM, Biggs SJ, Hanson TL, Nicolelis MA, Srinivasan MA. The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm. IEEE transactions on biomedical engineering. 2007 ;54(8):1520-9.
  26. Azab AM, Onsy A, El-Mahlawy MH. Design and development of a low cost prosthetic arm control system based on sEMG signal. InASME International Mechanical Engineering Congress and Exposition 2015; 57380: V003T03A080.
  27. available at: 12/November 2019
  28. Konrad P. The ABC of EMG: A practical introduction to kinesiological electromyography. 2005.
  29. Maleki A. Modeling and Reanimation Control of Paralysed Arm for C5/C6 SCI Patients Using. Faculty of Biomedical Engineering-Department of Bioelectric Ph.D Thesis. Amirkabir University of Technology, Tehran, Iran. 2009.
  30. Tang L, Li F, Cao S, Zhang X, Wu D, Chen X. Muscle synergy analysis in children with cerebral palsy. Journal of neural engineering. 2015;12(4):046017.


Volume 18, Issue 1
January and February 2021
Pages 30-39
  • Receive Date: 13 August 2019
  • Revise Date: 18 January 2020
  • Accept Date: 19 January 2020
  • First Publish Date: 01 January 2021