Direct comparison of muscle force predictions using linear and nonlinear programming.
Direct comparison of muscle force predictions using linear and nonlinear programming.
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使用线性和非线性编程直接比较肌肉力量预测。
DOI：
10.1115/1.3138669
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发表时间：
1987
期刊：
影响因子：
--
通讯作者：
Arora,JS
中科院分区：
文献类型：
--
作者：
Pedersen,DR;Brand,RA;Cheng,C;Arora,JS
估计运动过程中的肌肉和关节的力量需要与解决不确定问题的现有方法相一致的公式。由于模型公式、算法、输入数据和其他因素的差异很大，很难直接比较文献中提出的不同优化方法之间的结果。我们提出了一个新的优化程序，其中包括线性和非线性技术，允许各种成本函数和更大的灵活性问题制定的应用程序。统一的解决方案的方法，如这里演示的一个，提供直接评估等因素的优化标准和约束。这种统一的方法表明，非线性制剂（报道的种类）允许更多的协同活性，与线性制剂相比，允许拮抗活性。EMG活动和预测力的一致性在非线性预测中比线性预测更好。协同和拮抗活性的预测导致更高的联合力预测。放松肌肉解决整个节间力矩的要求，在非线性公式中保持肌肉协同作用，同时缓解肌肉拮抗作用并降低预测的关节接触力。这种统一的方法允许更多的可能性，探索新的优化配方，并在比较的解决方案，以前报道的配方。
Estimating forces in muscles and joints during locomotion requires formulations consistent with available methods of solving the indeterminate problem. Direct comparisons of results between differing optimization methods proposed in the literature have been difficult owing to widely varying model formulations, algorithms, input data, and other factors. We present an application of a new optimization program which includes linear and nonlinear techniques allowing a variety of cost functions and greater flexibility in problem formulation. Unified solution methods such as the one demonstrated here, offer direct evaluations of such factors as optimization criteria and constraints. This unified method demonstrates that nonlinear formulations (of the sort reported) allow more synergistic activity and in contrast to linear formulations, allow antagonistic activity. Concurrence of EMG activity and predicted forces is better with nonlinear predictions than linear predictions. The prediction of synergistic and antagonistic activity expectedly leads to higher joint force predictions. Relaxation of the requirement that muscles resolve the entire intersegmental moment maintains muscle synergism in the nonlinear formulation while relieving muscle antagonism and reducing the predicted joint contact force. Such unified methods allow more possibilities for exploring new optimization formulations, and in comparing the solutions to previously reported formulations.