Comparison of Independence of Triceps Brachii and Biceps Brachii Between Paretic and Non-paretic Side During Different MVCs—A Case Study

  • Ganesh NaikEmail author
  • Rifai Chai
  • Steven Su
  • Song Rong
  • Hung T. Nguyen
Part of the Series in BioEngineering book series (SERBIOENG)


Stroke is one of the major causes of permanent disability in adults. Physical training and rehabilitation help stroke survivors to carry out their day-to-day tasks. Surface electromyography (sEMG) has been widely used for stroke rehabilitation and assessment of muscle activities for different force levels. In this regard, it is very important to know the function and differences between various muscles involved in the stroke rehabilitation process. Hence, this study investigated the independence between biceps and triceps brachii for paretic and non-paretic sides during different muscle voluntary contractions (MVCs). Source separation technique using independent component analysis (ICA) and time domain features such as root mean square (RMS), mean absolute value (MAV), and integrated absolute value (IAV) were used to measure the muscle activities. The results show that biceps brachii muscles are more independent than triceps brachii muscles for different MVCs. The findings of this study could be used for measuring independence between muscles, which would help to identify and treat the specific muscle during stroke rehabilitation procedures.


Stroke rehabilitation Electromyography Independent component analysis RMS Triceps brachii Biceps brachii 


  1. 1.
    Aprile, I., Rabuffetti, M., Padua, L., Di Sipio, E., Simbolotti, C., Ferrarin, M.: Kinematic analysis of the upper limb motor strategies in stroke patients as a tool towards advanced neurorehabilitation strategies: a preliminary study. BioMed. Res. Int. 2014 (2014)CrossRefGoogle Scholar
  2. 2.
    Cesqui, B., Tropea, P., Micera, S., Krebs, H.I.: EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: a feasibility study. J. Neuroeng. Rehabil. 10, 1 (2013)CrossRefGoogle Scholar
  3. 3.
    Criswell, E.: Cram’s Introduction to Surface Electromyography. Jones & Bartlett Publishers (2010)Google Scholar
  4. 4.
    Dipietro, L., Ferraro, M., Palazzolo, J.J., Krebs, H.I., Volpe, B.T., Hogan, N.: Customized interactive robotic treatment for stroke: EMG-triggered therapy. IEEE Trans. Neural Syst. Rehabil. Eng. 13, 325–334 (2005)CrossRefGoogle Scholar
  5. 5.
    Farina, D., Jiang, N., Rehbaum, H., Holobar, A., Graimann, B., Dietl, H., Aszmann, O.C.: The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges. IEEE Trans. Neural Syst. Rehabil. Eng. 22, 797–809 (2014)CrossRefGoogle Scholar
  6. 6.
    Hermens, H.J., et al.: European recommendations for surface electromyography. Roessingh Res. Dev. 8, 13–54 (1999)Google Scholar
  7. 7.
    Hu, X., Tong, K.Y., Song, R., Tsang, V.S., Leung, P.O., Li, L.: Variation of muscle coactivation patterns in chronic stroke during robot-assisted elbow training. Arch. Phys. Med. Rehabil. 88, 1022–1029 (2007)CrossRefGoogle Scholar
  8. 8.
    Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Comput. 9, 1483–1492 (1997)CrossRefGoogle Scholar
  9. 9.
    Lee, T.-W., Girolami, M., Sejnowski, T.J.: Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput. 11, 417–441 (1999)CrossRefGoogle Scholar
  10. 10.
    Leonard, C., Gardipee, K.A., Koontz, J.R., Anderson, J.-H., Wilkins, S.A.: Correlation between impairment and motor performance during reaching tasks in subjects with spastic hemiparesis. J. Rehabil. Med. 38, 243 (2006)CrossRefGoogle Scholar
  11. 11.
    Li, X., Liu, J., Li, S., Wang, Y.C., Zhou, P.: Examination of hand muscle activation and motor unit indices derived from surface EMG in chronic stroke. IEEE Trans. Biomed. Eng. 61, 2891–2898 (2014)CrossRefGoogle Scholar
  12. 12.
    Miller, L.C., Dewald, J.P.: Involuntary paretic wrist/finger flexion forces and EMG increase with shoulder abduction load in individuals with chronic stroke. Clin. Neurophysiol. 123, 1216–1225 (2012)CrossRefGoogle Scholar
  13. 13.
    Naik, G.R., Kumar, D.K., Palaniswami, M.: Signal processing evaluation of myoelectric sensor placement in low-level gestures: sensitivity analysis using independent component analysis. Expert Syst. 31, 91–99 (2014)CrossRefGoogle Scholar
  14. 14.
    Simoneau, E.M., Longo, S., Seynnes, O.R., Narici, M.V.: Human muscle fascicle behavior in agonist and antagonist isometric contractions. Muscle Nerve 45, 92–99 (2012)CrossRefGoogle Scholar
  15. 15.
    Song, R., Tong, K.Y.: EMG and kinematic analysis of sensorimotor control for patients after stroke using cyclic voluntary movement with visual feedback. J. Neuroeng. Rehabil. 10, 1 (2013)Google Scholar
  16. 16.
    Sun, R., Song, R., Tong, K.: Complexity analysis of EMG signals for patients after stroke during robot-aided rehabilitation training using fuzzy approximate entropy. IEEE Trans. Neural Syst. Rehabil. Eng. 22, 1013–1019 (2014)CrossRefGoogle Scholar
  17. 17.
    Woodford, H., Price, C.: EMG biofeedback for the recovery of motor function after stroke. Cochrane Database Syst. Rev. 2 (2007)Google Scholar
  18. 18.
    Ziehe, A., Laskov, P., Nolte, G., Müller, K.R.: A fast algorithm for joint diagonalization with non-orthogonal transformations and its application to blind source separation. J. Mach. Learn. Res. 5, 777–800 (2004)MathSciNetzbMATHGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ganesh Naik
    • 1
    Email author
  • Rifai Chai
    • 2
  • Steven Su
    • 3
  • Song Rong
    • 4
  • Hung T. Nguyen
    • 2
  1. 1.MARCS Institute for Brain, Behaviour and Development Institute, Western Sydney UniversityPenrithAustralia
  2. 2.Faculty of Engineering and ITSwinburne University of TechnologyMelbourneAustralia
  3. 3.Faculty of Engineering and ITUniversity of Technology SydneySydneyAustralia
  4. 4.School of Biomedical EngineeringSun Yat-Sen UniversityGuangzhouChina

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