A two-step EMG-and-optimization process to estimate muscle force during dynamic movement

被引:34
作者
Amarantini, David [1 ]
Rao, Guillaume [2 ]
Berton, Eric [2 ]
机构
[1] Univ Toulouse, UPS, LAPMA, F-31062 Toulouse 9, France
[2] Univ Aix Marseille 2, CNRS, Inst Sci Mouvement EJ Marey, F-13288 Marseille, France
关键词
Muscle force; Muscle group moments; Cocontraction; Electromyography; Min/max optimization; Dynamic condition; JOINT MOMENTS; MUSCULOSKELETAL MODEL; CRUCIATE LIGAMENT; HYBRID APPROACH; KNEE-JOINT; IN-VIVO; CONTRACTION; RECRUITMENT; PREDICTIONS; PATTERNS;
D O I
10.1016/j.jbiomech.2010.02.025
中图分类号
Q6 [生物物理学];
学科分类号
071011 [生物物理学];
摘要
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. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1827 / 1830
页数:4
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