Skip to main content
SpringerLink
Log in

Skeletal-level control-based forward dynamic analysis of acquired healthy and assisted gait motion

  • Published:
Multibody System Dynamics Aims and scope Submit manuscript

Abstract

Gait analysis is commonly addressed through inverse dynamics. However, forward dynamics can be advantageous when descending to muscular level, as it allows activation and contraction equations to be integrated with motion thus providing better dynamic consistency, or when studying assisted gait, as it enables the estimation of the interaction forces between subject and devices even if the motion capture process doesn’t provide enough resolution to distinguish the motions of limb and device. Control-based methods seem to be the most natural choice to carry out the forward-dynamics analysis of an acquired gait, but several options exist in their application. The paper explores such options for healthy and assisted gait, and concludes that the computed torque control of all the subject’s degrees of freedom is the alternative that provides the most accurate results. Moreover, the study of its more problematic underactuated variant accompanied by contact models showed to be connected to neighbor challenging topics as gait prediction or walking simulation of humanoids.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26

Similar content being viewed by others

References

  1. Winter, D.A.: Biomechanics and Motor Control of Human Movement, 3rd edn. Wiley, New York (2005)

    Google Scholar 

  2. Abdel-Malek, K.A., Arora, J.S.: Human Motion Simulation. Academic Press, San Diego (2013)

    Google Scholar 

  3. Riemer, R., Hsiao-Wecksler, E.T.: Improving net joint torque calculations through a two-step optimization method for estimating body segment parameters. J. Biomech. Eng. 131(1), 011007 (2009)

    Google Scholar 

  4. Holmberg, L.J., Klarbring, A.: Muscle decomposition and recruitment criteria influence muscle force estimates. Multibody Syst. Dyn. 28(3), 283–289 (2012)

    Article  MathSciNet  Google Scholar 

  5. Thelen, D.G., Anderson, F.C.: Using computed muscle control to generate forward dynamic simulations of human walking from experimental data. J. Biomech. 39, 1107–1115 (2006)

    Article  Google Scholar 

  6. Michaud, F., Lugris, U., Ou, Y., Cuadrado, J., Kecskemethy, A.: Influence of muscle recruitment criteria on joint reaction forces during human gait. In: ECCOMAS Thematic Conference on Multibody Dynamics, Barcelona, Spain (2015)

    Google Scholar 

  7. Quental, C., Folgado, J., Ambrosio, J.: A window moving inverse dynamics optimization for biomechanics of motion. Multibody Syst. Dyn. 38(2), 157–171 (2016)

    Article  Google Scholar 

  8. Quental, C., Azevedo, M., Ambrosio, J., Gonçalves, S.B., Folgado, J.: Influence of the musculotendon dynamics on the muscle force sharing problem of the shoulder: a fully inverse dynamics approach. J. Biomed. Eng. 140(7), 071005 (2018)

    Article  Google Scholar 

  9. Ackermann, M., Schiehlen, W.: Physiological methods to solve the force-sharing problem in biomechanics. In: Bottasso, C.L. (ed.) Multibody Dynamics—Computational Methods and Applications, pp. 1–24. Springer, Berlin (2009)

    MATH  Google Scholar 

  10. Silva, P.C., Silva, M.T., Martins, J.M.: Evaluation of the contact forces developed in the lower limb/orthosis interface for comfort design. Multibody Syst. Dyn. 24(3), 367–388 (2010)

    Article  MATH  Google Scholar 

  11. Dallali, H., Mosadeghzad, M., Medrano-Cerda, G.A., Docquier, N., Kormushev, P., Tsagarakis, N., Li, Z., Caldwell, D.: Development of a dynamic simulator for a compliant humanoid robot based on a symbolic multibody approach. In: IEEE Int. Conference on Mechatronics, ICM, Vicenza, Italy (2013)

    Google Scholar 

  12. Ackermann, M., van den Bogert, A.J.: Optimality principles for model-based prediction of human gait. J. Biomech. 43(6), 1055–1060 (2010)

    Article  Google Scholar 

  13. Vaughan, C.L., Davis, B.L., O’Connor, J.C.: Dynamics of Human Gait, 2nd edn. Kiboho Publishers, Cape Town (1999)

    Google Scholar 

  14. Ambrosio, J.A.C., Kecskemethy, A.: Multibody dynamics of biomechanical models for human motion via optimization. In: Garcia Orden, J.C., Goicolea, J.M., Cuadrado, J. (eds.) Multibody Dynamics—Computational Methods and Applications, pp. 245–270. Springer, Berlin (2007)

    Chapter  Google Scholar 

  15. Lugris, U., Carlin, J., Luaces, A., Cuadrado, J.: Gait analysis system for spinal cord injured subjects assisted by active orthoses and crutches. J. Multi-Body Dyn. 227(4), 363–374 (2013)

    Google Scholar 

  16. https://www.iftomm-multibody.org/benchmark/, Library of Computational Benchmark Problems (problem Gait 2D)

  17. Garcia de Jalon, J., Bayo, E.: Kinematic and Dynamic Simulation of Multibody Systems. Springer, New York (1994)

    Book  Google Scholar 

  18. Gupta, K.C.: Mechanics and Control of Robots. Springer, New York (1997)

    Google Scholar 

  19. Seifried, R.: Integrated mechanical and control design of underactuated multibody systems. Nonlinear Dyn. 67, 1539–1557 (2012)

    Article  MathSciNet  Google Scholar 

  20. Blajer, W.: The use of servo-constraints in the inverse dynamics analysis of underactuated multibody systems. J. Comput. Nonlinear Dyn. 9(4), 041008 (2014)

    Article  Google Scholar 

  21. Seth, A., Pandy, M.G.: A neuromusculoskeletal tracking method for estimating individual muscle forces in human movement. J. Biomech. 40, 356–366 (2007)

    Article  Google Scholar 

  22. Lugris, U., Carlin, J., Pamies-Vila, R., Font-Llagunes, J.M., Cuadrado, J.: Solution methods for the double-support indeterminacy in human gait. Multibody Syst. Dyn. 30(3), 247–263 (2013)

    Article  MathSciNet  Google Scholar 

  23. Shourijeh, M.S., McPhee, J.: Foot–ground contact modeling within human gait simulations: from Kelvin–Voigt to hyper-volumetric models. Multibody Syst. Dyn. 35(4), 393–407 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  24. Hansen, N.: The CMA evolution strategy: a comparing review. Stud. Fuzziness Soft Comput. 192, 75–102 (2006)

    Article  Google Scholar 

  25. Dopico, D., Luaces, A., Gonzalez, M., Cuadrado, J.: Dealing with multiple contacts in a human-in-the-loop application. Multibody Syst. Dyn. 25(2), 167–183 (2011)

    Article  MathSciNet  Google Scholar 

  26. Flores, P., Machado, M., Silva, M.T., Martins, J.M.: On the continuous contact force model for soft materials in multibody dynamics. Multibody Syst. Dyn. 25(3), 357–375 (2011)

    Article  MATH  Google Scholar 

  27. Englsberger, J., Ott, C., Albu-Schäffer, A.: Three-dimensional bipedal walking control based on divergent component of motion. IEEE Trans. Robot. 31(2), 355–368 (2015)

    Article  Google Scholar 

  28. Dehghani, R., Fattah, A., Abedi, E.: Cyclic gait planning and control of a five-link biped robot with four actuators during single support and double support phases. Multibody Syst. Dyn. 33(4), 389–411 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  29. Wang, J.M., Hamner, S.R., Delp, S.L., Koltun, V.: Optimizing locomotion controllers using biologically-based actuators and objectives. ACM Trans. Graph. 31(4), 25 (2012)

    Google Scholar 

  30. Geijtenbeek, T., van de Panne, M., van der Stappen, A.F.: Flexible muscle-based locomotion for bipedal creatures. ACM Trans. Graph. 32(6), 206 (2013)

    Article  Google Scholar 

Download references

Compliance with Ethical Standards

Research involving human participants: This study was approved by the institutional ethical committee.

Informed consent: All participants in the experiments gave their informed consent.

Funding: This study was funded by the Spanish Ministry of Economy and Competitiveness (MINECO) under project DPI2015-65959-C3-1-R, cofinanced by the European Union through EFRD program.

Conflict of interest: J. Cuadrado is a member of the Editorial Board of the journal Multibody System Dynamics. The remaining authors have no conflict of interest.

Author information

Authors and Affiliations

  1. Escuela Politecnica Superior, University of La Coruña, Ferrol, Spain

    F. Mouzo, U. Lugris & J. Cuadrado

  2. Dept. of Mechanical Engineering, Universitat Politecnica de Catalunya, Barcelona, Spain

    R. Pamies-Vila

Authors
  1. F. Mouzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. U. Lugris
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Pamies-Vila
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. Cuadrado
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Cuadrado.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mouzo, F., Lugris, U., Pamies-Vila, R. et al. Skeletal-level control-based forward dynamic analysis of acquired healthy and assisted gait motion. Multibody Syst Dyn 44, 1–29 (2018). https://doi.org/10.1007/s11044-018-09634-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11044-018-09634-4

Keywords

Search

Navigation