Université de Toulouse, UPS, LAPMA, 118 Route de Narbonne, 31062 Toulouse Cedex 9, France.
J Biomech. 2010 Jun 18;43(9):1827-30. doi: 10.1016/j.jbiomech.2010.02.025. Epub 2010 Mar 5.
The present study proposed a two-step EMG-and-optimization method for muscle force estimation in dynamic condition. Considering the strengths and the limitations of existing methods, the proposed approach exploited the advantages of min/max optimization with constraints on the contributions of the flexor and extensor muscle groups to the net joint moment estimated through an EMG-to-moment approach. Our methodology was tested at the knee joint during dynamic half squats, and was compared with traditional min/max optimization. In general, results showed significant differences in muscle force estimates from EMG-and-optimization method when compared with those from traditional min/max optimization. Muscle forces were higher - especially in the antagonist muscles - and more consistent with EMG patterns because of the ability of the proposed approach to properly account for agonist/antagonist cocontraction. In addition, muscle forces agree with mechanical constraints regarding the net, the agonist, and the antagonist moments, thus greatly improving the confidence in muscle force estimates. The proposed two-step EMG-and-optimization method for muscle force estimation is easy to implement with relatively low computational requirements and, thus, could offer interesting advantages for various applications in many fields, including rehabilitation, clinical, and sports biomechanics.
本研究提出了一种两步肌电-优化方法，用于动态条件下的肌肉力量估计。考虑到现有方法的优缺点，该方法利用了带约束的最小/最大优化的优势，该约束限制了屈肌和伸肌群体对通过肌电-力矩方法估计的净关节力矩的贡献。我们的方法在动态半蹲时在膝关节进行了测试，并与传统的最小/最大优化进行了比较。一般来说，结果表明，与传统的最小/最大优化相比，肌电-优化方法的肌肉力量估计存在显著差异。由于该方法能够正确考虑到协同收缩，因此肌肉力量更高-尤其是在拮抗肌中-并且与肌电模式更一致。此外，肌肉力量与净力矩、主动肌和拮抗肌力矩的力学约束一致，从而极大地提高了肌肉力量估计的可信度。所提出的两步肌电-优化方法易于实施，计算要求相对较低，因此在康复、临床和运动生物力学等多个领域的各种应用中可能具有很大的优势。
