Assessment of Antagonistic Muscle Forces During Forearm Flexion/Extension

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


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.


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.



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.


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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

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