A method to combine numerical optimization and EMG data for the estimation of joint moments under dynamic conditions
A method to combine numerical optimization and EMG data for the estimation of joint moments under dynamic conditions
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DOI：
10.1016/j.jbiomech.2003.12.020
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发表时间：
2004-09-01
影响因子：
2.4
通讯作者：
Martin, L
中科院分区：
文献类型：
--
作者：
Amarantini, D;Martin, L
为了解决相对肌群水平的肌肉冗余问题，必须采用一种替代逆动力学的方法。考虑到现有替代方案的优势，本研究旨在利用肌电图（EMG）信号结合非线性约束优化在单一程序中计算动态条件下的膝关节力矩。相关的数学问题解释了肌肉行为，试图获得准确的结果力矩预测，以及生理上对激动剂和拮抗剂力矩的现实估计。实验方案包括(1)等距试验，以确定最有效的肌电信号处理方法，以预测合成力矩；(2)就地试验，从处理后的肌电信号中计算动态条件下的关节力矩。将模型预测与基于生物的模型的输出进行定量比较，表明所提出的方法(1)产生了对结果力矩的最准确估计，(2)通过实施适当的约束避免了可能的不一致。作为解决动态条件下冗余问题的可能解决方案，所提出的优化公式也导致了激动剂和拮抗剂力矩的现实预测。(C) 2004 Elsevier Ltd.版权所有。
To solve the problem of muscle redundancy at the level of opposing muscle groups, an alternative method to inverse dynamics must be employed. Considering the advantages of existing alternatives, the present study was aimed to compute knee joint moments under dynamic conditions using electromyographic (EMG) signals combined with non-linear constrained optimization in a single routine. The associated mathematical problems accounted for muscle behavior in an attempt to obtain accurate predictions of the resultant moment as well as physiologically realistic estimates of agonist and antagonist moments. The experiment protocol comprised (1) isometric trials to determine the most effective EMG processing for the prediction of the resultant moment and (2) stepping-in-place trials for the calculation of joint moments from processed EMG under dynamic conditions. Quantitative comparisons of the model predictions with the output of a biological-based model, showed that the proposed method (1) produced the most accurate estimates of the resultant moment and (2) avoided possible inconsistencies by enforcing appropriate constraints. As a possible solution for solving the redundancy problem under dynamic conditions, the proposed optimization formulation also led to realistic predictions of agonist and antagonist moments. (C) 2004 Elsevier Ltd. All rights reserved.