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使用基于运动学和肌电图数据的成本函数来量化肌肉力量。

Using a cost function based on kinematics and electromyographic data to quantify muscle forces.

作者信息

Wen J, Raison M, Achiche S

机构信息

Mechanical Engineering Department, Polytechnique Montreal, Montreal, Canada; Rehabilitation Engineering Chair Applied to Pediatrics (RECAP), Polytechnique Montreal and Ste-Justine UHC, Canada.

Mechanical Engineering Department, Polytechnique Montreal, Montreal, Canada; Rehabilitation Engineering Chair Applied to Pediatrics (RECAP), Polytechnique Montreal and Ste-Justine UHC, Canada.

出版信息

J Biomech. 2018 Oct 26;80:151-158. doi: 10.1016/j.jbiomech.2018.09.002. Epub 2018 Sep 11.

Abstract

A reliable evaluation of muscle forces in the human body is highly desirable for several applications in both clinical and research contexts. Several models of muscle force distribution based on non-invasive measurements have been proposed since 1836 (Weber and Weber, 1836), amongst which Crowninshield's model (Crowninshield and Brand, 1981), which maximizes a cost-function representing the muscle fiber endurance, is the most popular. It is worth noting that Crowninshield's model is the most widely adopted notwithstanding its major limitations of physiological coherence. Forster et al. (2004) pointed out that "these (conventional) criteria however do not predict co-contraction adequately". Besides, electromyographic (EMG)-driven models have been proposed to assess individual muscle forces, which have not been broadly adopted due to their complexity and the need for a calibration before each test. In this context, a cost function based on kinematic and electromyographic data could provide the advantage of being physiologically more coherent with muscle activations compared to conventional cost-functions based on kinematics solely, and easier to use than the EMG-driven models. The objective of this study is to propose the first cost-function based on kinematics and electromyographic data to quantify muscle forces. When applying this new cost-function on a database of upper limb motions data of 17 subjects, healthy or with cerebral palsy, the muscle force prediction of the proposed model was 17.74% more coherent with the EMG pattern than the prediction of Crowninshield's model. And on average, these results were more consistent whether the subjects were healthy or with cerebral palsy. In conclusion, we propose this cost-function for the quantification of muscle forces.

摘要

在临床和研究领域的多种应用中,非常需要对人体肌肉力量进行可靠评估。自1836年(韦伯和韦伯,1836年)以来,已经提出了几种基于非侵入性测量的肌肉力量分布模型,其中最受欢迎的是克劳宁希尔德模型(克劳宁希尔德和布兰德,1981年),该模型使代表肌肉纤维耐力的成本函数最大化。值得注意的是,尽管克劳宁希尔德模型存在生理连贯性方面的主要局限性,但它仍是应用最广泛的模型。福斯特等人(2004年)指出,“然而,这些(传统)标准并不能充分预测协同收缩”。此外,还提出了肌电图(EMG)驱动的模型来评估单个肌肉力量,但由于其复杂性以及每次测试前都需要进行校准,这些模型尚未得到广泛应用。在这种情况下,与仅基于运动学的传统成本函数相比,基于运动学和肌电图数据的成本函数可能具有在生理上与肌肉激活更具连贯性的优势,并且比EMG驱动的模型更易于使用。本研究的目的是提出第一个基于运动学和肌电图数据的成本函数来量化肌肉力量。当将这个新的成本函数应用于17名健康或患有脑瘫的受试者的上肢运动数据数据库时,所提出模型的肌肉力量预测与EMG模式的连贯性比克劳宁希尔德模型的预测高17.74%。而且平均而言,无论受试者是健康还是患有脑瘫,这些结果都更一致。总之,我们提出这个成本函数用于肌肉力量的量化。

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