Assessment of Antagonistic Muscle Forces During Forearm Flexion/Extension

  • Maxime Raison
  • Christine Detrembleur
  • Paul Fisette
  • Jean-Claude Samin
Chapter
Part of the Computational Methods in Applied Sciences book series (COMPUTMETHODS, volume 23)

Abstract

Today, the accurate assessment of muscle forces performed by the human body in motion is still expected for many clinical applications and studies. However, as most of the joints are overactuated by several muscles, any non-invasive muscle force quantification needs to solve a redundancy problem. Consequently, the aim of this study is to propose a non-invasive method to assess muscle forces in the human body during motion, using a multibody model-based optimization process that attempts to solve the agonistic and antagonistic muscle overactuation. The main originality of the proposed method is the cautious using of Electromyographic (EMG) data information, known by all to be noisy-corrupted, via a protocol divided into two main steps:
  1. 1.

    Muscle force static calibration,

     
  2. 2.

    Muscle force dynamical quantification.

     

In this chapter, the process is applied to a benchmark case: the force quantification of the elbow flexor and extensor muscle sets of subjects engaged in weightlifting and performing cycles of forearm flexion/extension. A statistical validation of this method shows a good inter-test reproducibility and a very good correlation between a. the net joint torques resulting from the obtained muscle forces and b. the net joint torques given by inverse dynamics.Consequently, since the method is able to consider measured information on the actual muscle activation, it becomes a promising alternative to methods based on preset strategies, usually presented in literature, such as the strategy that maximizes endurance defined by Crowninshield et al.

Keywords

Muscle Force Biceps Brachii Joint Torque Inverse Dynamic Spherical Joint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors are grateful to Pr. P.Y. Willems, Emeritus of the Université catholique de Louvain, Louvain-la-Neuve, Belgium, for his help and support.

References

  1. 1.
    Amarantini D (2003) Estimation des Efforts Musculaires Partir des Donnes Priphriques: Application l’Analyse de la Coordination Pluri-Articulaire. Thesis of the Universit Joseph Fourier, Grenoble, FranceGoogle Scholar
  2. 2.
    Anderson FC, Pandy MG (2001) Dynamic optimization of human walking. J Biomech Eng 123(5):381–390CrossRefGoogle Scholar
  3. 3.
    Bao H, Willems PY (1999) On the kinematic modelling and the parameter estimation of the human shoulder. J Biomech 32:943–950CrossRefGoogle Scholar
  4. 4.
    Blajer W, Czaplicki A (2001) Modeling and inverse simulation of somersaults on the trampoline. J Biomech 34(12):1619–1629CrossRefGoogle Scholar
  5. 5.
    Bouisset S (2002) Biomécanique et physiologie du mouvement. Abrégés, Editions Masson, Paris, FranceGoogle Scholar
  6. 6.
    Bouisset S, Le Bozec S, Ribreau C (2002) Postural dynamics in maximal isometric ramp efforts. Biol Cybern 87(3):211–219MATHCrossRefGoogle Scholar
  7. 7.
    BTS Bioengineering (2010). http://www.btsbioengineering.com/
  8. 8.
    Buchanan TS, Lloyd DG, Besier TF (2004) Neuromusculoskeletal modeling: estimation of muscle forces moments and movements measurements of neural command. J Appl Biomech 20:367–395Google Scholar
  9. 9.
    Cappozzo A, Leo T, Pedotti A (1975) A general computing method for the analysis of human locomotion. J Biomech 8:307–320CrossRefGoogle Scholar
  10. 10.
    Challis JH, Kerwin DG (1996) Quantification of the uncertainties in resultant joint moments computed in a dynamic activity. J Sports Sci 14:219–231CrossRefGoogle Scholar
  11. 11.
    Chenut X, Fisette P, Samin JC (2002) Recursive formalism with a minimal dynamic parametrization for the identification and simulation of multibody systems. Application to the Human Body. Multibody Syst Dyn 8:117–140MATHCrossRefGoogle Scholar
  12. 12.
    Chze L, Fregly BJ, Dimnet J (1995) A solidification procedure to facilitate kinematic analyses based on video system data. J Biomech 28:879–884CrossRefGoogle Scholar
  13. 13.
    Crowninshield RD, Brand RA (1981) A physiologically based criterion of muscle force prediction in locomotion. J Biomech 14(11):793–801CrossRefGoogle Scholar
  14. 14.
    Debril JF, Pudlo P, El Menceur M, Gorce P, Lepoutre FX (2007) Human articulation efforts estimation in the automobile vehicle accessibility movement – A pilot study. In: Proceedings of the 1st international conference on digital human modeling, computer science, Beijing, China, 22–27 JulyGoogle Scholar
  15. 15.
    De Groote F, Pipeleers G, Demeulenaere B, Jonckers I, Spaepen P, Swevers J, De Schutter J (2006) A convex optimization approach to dynamic musculoskeletal analysis. In: Proceedings CD of the 6th national congress on theoretical and applied mechanics, Ghent, Belgium, 29–30 MayGoogle Scholar
  16. 16.
    De Groote F, Pipeleers G, Jonkers I, Demeulenaere B, Swevers J, De Schutter J (2007) Physiology based inverse dynamic analysis of normal and hemiparetic gait. In: Proceedings of the 16th annual meeting of ESMAC, gait & posture 26(S17), Athens, Greece, 24–29 SeptemberGoogle Scholar
  17. 17.
    De Jalón G, Bayo E (1993) Kinematic and dynamic simulation of multibody systems: the real-time challenge. Springer, New YorkGoogle Scholar
  18. 18.
    De Leva P (1996) Adjustments to zatsiorsky-seluyanov’s segment inertia parameters. J Biomech 29(9):1223–1230CrossRefGoogle Scholar
  19. 19.
    Denoth J, Gruber K, Ruder H, Keppler M (1984) Forces and torques during sport activities with high accelerations. Perren SM, Scnheider E (eds) Biomechanics current interdisciplinary research. Martinuis Nijhoff Publishers, Dodrecht, Netherlands, pp 663–668Google Scholar
  20. 20.
    Dul J, Johnson GE, Shiavi R, Townsend MA (1984) Muscular synergism II. A minimum-fatigue criterion for load sharing between synergistic muscles. J Biomech 17(9):675–84Google Scholar
  21. 21.
    Epstein M, Herzog W (1998) Theoretical models of skeletal muscle. Wiley, Chichester, EnglandGoogle Scholar
  22. 22.
    Gergersen CS, Hull ML (2003) Non-driving intersegmental knee moments in cycling computed using a model that includes three-dimensional kinematics of the shank/foot and the effect of simplifying assumptions. J Biomech 36:803–813CrossRefGoogle Scholar
  23. 23.
    He J, Levine WS, Loeb GE (1991) Feedback gains for correcting small perturbations to standing posture. IEEE T Automat Contr 36:322–332MATHCrossRefGoogle Scholar
  24. 24.
    Holzbaur KRS, Murray WM, Delp SL (2005) A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control. Ann Biomed Eng 33(6): 829–840CrossRefGoogle Scholar
  25. 25.
    Lloyd DG, Bessier TF (2003) An emg-driven musculoskeletal model to estimate muscle forces and knee joint moment in vivo. J Biomech 36:765–776CrossRefGoogle Scholar
  26. 26.
    Nigg BM, Herzog W (eds) (1999) Biomechanics of the musculo-skeletal system, 2nd edn. Chichester, England, and Wiley, New YorkGoogle Scholar
  27. 27.
    Penrod DD, Davy DT, Singh DP (1974) An optimization approach to tendon force analysis. J Biomech 7:123–129CrossRefGoogle Scholar
  28. 28.
    Pérez M, Ausejo S, Pargada J, Suescun A, Celigeta JT (2003) Application of multibody system analysis for the evaluation of the driver’s discomfort. In: Proceedings of the multibody dynamics, Lisbon, Portugal, 1–4 JulyGoogle Scholar
  29. 29.
    Raasch CC, Zajac FE, Ma B, Levine WS (1997) Muscle coordination of maximum-speed pedaling. J Biomech 30(6):595–602CrossRefGoogle Scholar
  30. 30.
    Raison M, Aubin CE, Detrembleur C, Fisette P, Samin JC (2008) Quantification of intervertebral efforts during walking: comparison between a healthy and a scoliotic subject. Stud Health Tech Informat 140:61–64Google Scholar
  31. 31.
    Raison M, Detrembleur C, Fisette P, Samin JC, Willems PY (2005) Determination of joint kinematics and dynamics in the human body: application to a subject getting up from a seat. In: Proceedings of Eccomas thematic conference on advances in computational multibody dynamics, Madrid, Spain, 21–24 JuneGoogle Scholar
  32. 32.
    Raison M, Detrembleur C, Fisette P, Samin JC, Willems PY (2006) Estimation of human muscular efforts using a model based optimization method. In: Proceedings CD of the 7th national congress on theoretical and applied mechanics, Mons, Belgium, 29–30 MayGoogle Scholar
  33. 33.
    Raison M, Gaudez C, Le Bozec S, Willems PY (2007) Determination of joint efforts in the human body during maximum ramp pushing efforts. J Biomech 40(3):627–33CrossRefGoogle Scholar
  34. 34.
    Samin JC, Fisette P (2003) Symbolic modeling of multibody systems. Kluwer, Dordrecht, NetherlandsMATHGoogle Scholar
  35. 35.
    Seireg A, Arvikar RJ (1973) A mathematical model for evaluation of force in lower extremities of the musculo-skeletal system. J Biomech 6:313–326CrossRefGoogle Scholar
  36. 36.
    Silva M, Ambrósio J (2004) Sensitivity of the results produced by the inverse dynamic analysis of a human stride to perturbed input data. Gait Posture 19(1):35–49CrossRefGoogle Scholar
  37. 37.
    Silva M, Ambrósio J, Pereira M (1997) A multibody approach to the vehicle and occupant integrated simulation. Int J Crashworthines 2(1):73–90CrossRefGoogle Scholar
  38. 38.
    Thelen DG (2003) Adjustment of muscle mechanics model parameters to simulate dynamic contractions in older adults. Trans ASME 125:70–77Google Scholar
  39. 39.
    Umberger BR, Gerritsen KGM, Martin PE (2003) A model of human energy expenditure. Comput Meth Biomech Biomed Eng 6(2):99–111CrossRefGoogle Scholar
  40. 40.
    Venture G, Yamane K, Nakamura Y (2005) Identifying musculo-tendon parameters of human body based on the musculo-skeletal dynamics computation and Hill-Stroeve muscle model. In: Proceedings of 5th IEEE-RAS international conference on humanoid robots, Tsukuba, Japan, 5–7 DecemberGoogle Scholar
  41. 41.
    Weber W, Weber E (1836) Mechanik der Menschlichen Gehwerkzauge, Gottingen. Gottinger, GermanyGoogle Scholar
  42. 42.
    Willems PY (ed) (1979) Introduction la mcanique. Masson, Paris, FranceGoogle Scholar
  43. 43.
    Winters JM (1990) Hill-based muscle models: a systems engineering perspective. Winters JM, Woo SLY (eds) Multiple muscle systems: biomechanics and movement organization. Springer, New YorkGoogle Scholar
  44. 44.
    Winters JM (1995) An improved muscle-reflex actuator for use in large-scale neuromusculoskeletal models. Ann Biomed Eng 23:359–374CrossRefGoogle Scholar
  45. 45.
    Zacher I (2004) Strength Based Discomfort Model of Posture and Movement. In: Proceedings of the SAE international digital human modelling conference, Rochester, Michigan, USA, 15–17 JuneGoogle Scholar
  46. 46.
    Zajac FE (1989) Muscle and tendon: properties, models, scaling and application to biomechanics and motor control. Crit Rev Biomed Eng 17(4):359–411Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Maxime Raison
    • 1
  • Christine Detrembleur
    • 2
  • Paul Fisette
    • 1
  • Jean-Claude Samin
    • 1
  1. 1.Center for Research in Mechatronics (CEREM)École Polytechnique de Louvain – Université catholique de LouvainLouvain-la-NeuveBelgium
  2. 2.Rehabilitation and Physical Medicine Unit (READ)Université catholique de LouvainBruxellesBelgium

Personalised recommendations