A Subject-Specific EMG-Driven Musculoskeletal Model for the Estimation of Moments in Ankle Plantar-Dorsiflexion Movement

  • Congsheng Zhang
  • Qingsong Ai
  • Wei Meng
  • Jiwei Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)


In traditional rehabilitation process, ankle movement ability is only qualitatively estimated by its motion performance, however, its movement is actually achieved by the forces acting on the joints produced by muscles contraction. In this paper, the musculoskeletal model is introduced to provide a more physiologic method for quantitative muscle forces and muscle moments estimation during rehabilitation. This paper focuses on the modeling method of musculoskeletal model using electromyography (EMG) and angle signals for ankle plantar-dorsiflexion (P-DF) which is very important in gait rehabilitation and foot prosthesis control. Due to the skeletal morphology differences among people, a subject-specific geometry model is proposed to realize the estimation of muscle lengths and muscle contraction force arms. Based on the principle of forward and inverse dynamics, difference evolutionary (DE) algorithm is used to adjust individual parameters of the whole model, realizing subject-specific parameters optimization. Results from five healthy subjects show the inverse dynamics joint moments are well predicted with an average correlation coefficient of 94.21% and the normalized RMSE of 12.17%. The proposed model provides a good way to estimate muscle moments during movement tasks.


EMG signals Musculoskeletal model Ankle plantar-dorsiflexion Joint moment 



Research supported by The Excellent Dissertation Cultivation Funds of Wuhan University of Technology with No. 2016-YS-062 and National Natural Science Foundation of China under grants Nos. 51475342 and 61401318.


  1. 1.
    Zhang, T.: Stroke rehabilitation in China (2011 edition). Chin. J. Rehabil. Theor. Pract. 18(4), 301–318 (2012). (in Chinese)Google Scholar
  2. 2.
    Meng, W., Xie, S., Liu, Q., et al.: Robust iterative feedback tuning control of a compliant rehabilitation robot for repetitive ankle training. IEEE/ASME Trans. Mechatron. 22(1), 173–184 (2017)CrossRefGoogle Scholar
  3. 3.
    Vivian, M., Tagliapietra, L., Reggiani, M., et al.: Design of a subject-specific EMG model for rehabilitation movement. Biosyst. Biorobotics 7, 813–822 (2014)CrossRefGoogle Scholar
  4. 4.
    Patar, A., Jamlus, N., Makhtar, K., et al.: Development of dynamic ankle foot orthosis for therapeutic application. Procedia Eng. 41, 1432–1440 (2012)CrossRefGoogle Scholar
  5. 5.
    Meng, W., Ding, B., Zhou, Z., et al.: An EMG-based force prediction and control approach for robot-assisted lower limb rehabilitation. In: 45th IEEE International Conference on Systems, Man, and Cybernetics, pp. 2198–2203. Institute of Electrical and Electronics Engineers Inc., San Diego (2014)Google Scholar
  6. 6.
    Ai, Q., Ding, B., Liu, Q., et al.: A subject-specific EMG-driven musculoskeletal model for applications in lower-limb rehabilitation robotics. Int. J. Humanoid Robot. 13(03), 1650005 (2016)CrossRefGoogle Scholar
  7. 7.
    Hassani, W., Mohammed, S., Rifaï, H., et al.: Powered orthosis for lower limb movements assistance and rehabilitation. Control Eng. Pract. 26(1), 245–253 (2014)CrossRefGoogle Scholar
  8. 8.
    Kurt, M., Karin, G., Buchanan, T.: A real-time EMG-driven musculoskeletal model of the ankle. Multibody Sys. Dyn. 28(1–2), 169–180 (2012)Google Scholar
  9. 9.
    Zhang, M., Meng, W., Davies, T., et al.: A robot-driven computational model for estimating passive ankle torque with subject-specific adaptation. IEEE Trans. Biomed. Eng. 63(4), 814–821 (2016)Google Scholar
  10. 10.
    Prinold, J., Mazzà, C., Marco, R., et al.: A patient-specific foot model for the estimate of ankle joint forces in patients with juvenile idiopathic arthritis. Ann. Biomed. Eng. 44(1), 247–257 (2016)CrossRefGoogle Scholar
  11. 11.
    Fleischer, C., Hommel, G.: A human-exoskeleton interface utilizing electromyography. IEEE Trans. Robot. 24(4), 872–882 (2008)CrossRefGoogle Scholar
  12. 12.
    Delp, S., Anderson, F., Arnold, A., et al.: OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Bio-med. Eng. 54(11), 1940–1950 (2007)CrossRefGoogle Scholar
  13. 13.
    Zheng, R., Liu, T., Kyoko, S., et al.: In vivo estimation of dynamic muscle-tendon moment arm length using a wearable sensor system. In: 12th IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 647–652. Institute of Electrical and Electronics Engineers Inc., Xi’an (2008)Google Scholar
  14. 14.
    Delp, S., Loan, J., Hoy, M., et al.: An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures. IEEE Trans. Biomed. Eng. 37(8), 757–767 (1990)CrossRefGoogle Scholar
  15. 15.
    Shao, Q., Bassett, D., Manal, K., et al.: An EMG-driven model to estimate muscle forces and joint moments in stroke patients. Comput. Biol. Med. 39(12), 1083–1088 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Congsheng Zhang
    • 1
    • 2
  • Qingsong Ai
    • 1
    • 2
  • Wei Meng
    • 1
    • 2
  • Jiwei Hu
    • 1
    • 2
  1. 1.School of Information EngineeringWuhan University of TechnologyWuhanChina
  2. 2.Key Laboratory of Fiber Optic Sensing Technology and Information ProcessingWuhan University of Technology, Ministry of EducationWuhanChina

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