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在静态指尖力产生过程中,利用肌电图(EMG)数据来约束优化程序可改善手指肌腱张力估计。

Using EMG data to constrain optimization procedure improves finger tendon tension estimations during static fingertip force production.

作者信息

Vigouroux Laurent, Quaine Franck, Labarre-Vila Annick, Amarantini David, Moutet François

机构信息

Laboratoire Mouvement et Perception, UMR 6152, Université de la Méditerranée, Marseille, France.

出版信息

J Biomech. 2007;40(13):2846-56. doi: 10.1016/j.jbiomech.2007.03.010. Epub 2007 May 7.

Abstract

Determining tendon tensions of the finger muscles is crucial for the understanding and the rehabilitation of hand pathologies. Since no direct measurement is possible for a large number of finger muscle tendons, biomechanical modelling presents an alternative solution to indirectly evaluate these forces. However, the main problem is that the number of muscles spanning a joint exceeds the number of degrees of freedom of the joint resulting in mathematical under-determinate problems. In the current study, a method using both numerical optimization and the intra-muscular electromyography (EMG) data was developed to estimate the middle finger tendon tensions during static fingertip force production. The method used a numerical optimization procedure with the muscle stress squared criterion to determine a solution while the EMG data of three extrinsic hand muscles serve to enforce additional inequality constraints. The results were compared with those obtained with a classical numerical optimization and a method based on EMG only. The proposed method provides satisfactory results since the tendon tension estimations respected the mechanical equilibrium of the musculoskeletal system and were concordant with the EMG distribution pattern of the subjects. These results were not observed neither with the classical numerical optimization nor with the EMG-based method. This study demonstrates that including the EMG data of the three extrinsic muscles of the middle finger as inequality constraints in an optimization process can yield relevant tendon tensions with regard to individual muscle activation patterns, particularly concerning the antagonist muscles.

摘要

确定手指肌肉的肌腱张力对于理解和治疗手部疾病至关重要。由于大量手指肌肉肌腱无法直接测量,生物力学建模提供了一种间接评估这些力的替代解决方案。然而,主要问题是跨越关节的肌肉数量超过了关节的自由度数量,导致数学上的欠定问题。在当前研究中,开发了一种结合数值优化和肌内肌电图(EMG)数据的方法,以估计静态指尖力产生过程中中指肌腱的张力。该方法使用具有肌肉应力平方准则的数值优化程序来确定解决方案,同时来自手部三块外在肌肉的EMG数据用于施加额外的不等式约束。将结果与通过经典数值优化和仅基于EMG的方法获得的结果进行比较。所提出的方法提供了令人满意的结果,因为肌腱张力估计符合肌肉骨骼系统的力学平衡,并且与受试者的EMG分布模式一致。无论是经典数值优化方法还是基于EMG的方法都未观察到这些结果。这项研究表明,在优化过程中将中指三块外在肌肉的EMG数据作为不等式约束纳入,可以根据个体肌肉激活模式得出相关的肌腱张力,特别是对于拮抗肌。

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