Determination of muscle forces during motion can help to understand motor control, assess pathological movement, diagnose neuromuscular disorders, or estimate joint loads. Difficulty of in vivo measurement made computational analysis become a common alternative in which, as several muscles serve each degree of freedom, the muscle redundancy problem must be solved. Unlike static optimization (SO), synergy optimization (SynO) couples muscle activations across all time frames, thereby altering estimated muscle co-contraction. This study explores whether the use of a muscle synergy structure within an SO framework improves prediction of muscle activations during walking. A motion/force/electromyography (EMG) gait analysis was performed on five healthy subjects. A musculoskeletal model of the right leg actuated by 43 Hill-type muscles was scaled to each subject and used to calculate joint moments, muscle–tendon kinematics, and moment arms. Muscle activations were then estimated using SynO with two to six synergies and traditional SO, and these estimates were compared with EMG measurements. Synergy optimization neither improved SO prediction of experimental activation patterns nor provided SO exact matching of joint moments. Finally, synergy analysis was performed on SO estimated activations, being found that the reconstructed activations produced poor matching of experimental activations and joint moments. As conclusion, it can be said that, although SynO did not improve prediction of muscle activations during gait, its reduced dimensional control space could be beneficial for applications such as functional electrical stimulation or motion control and prediction.

运动过程中肌肉力量的确定有助于理解运动控制，评估病理运动，诊断神经肌肉疾病或估计关节负荷。难点体内测量的结果使计算分析成为一种常见的替代方法，其中，由于数个肌肉为每个自由度服务，因此必须解决肌肉冗余问题。与静态优化（SO）不同，协同优化（SynO）在所有时间范围内耦合肌肉激活，从而改变估计的肌肉共收缩。这项研究探讨了在SO框架内使用肌肉协同结构是否能改善步行过程中肌肉激活的预测。对五个健康受试者进行了运动/力/肌电图（EMG）步态分析。对每个受试者按比例缩放由43个Hill型肌肉致动的右腿的肌肉骨骼模型，并用于计算关节力矩，肌腱运动学和力矩臂。然后使用具有2至6种协同作用的SynO和传统的SO估算肌肉的激活，并将这些估计值与EMG测量值进行比较。协同优化既不能改善SO对实验激活模式的预测，也不能提供SO对关节力矩的精确匹配。最后，对SO估计的激活进行了协同分析，发现重建的激活产生的实验激活和关节力矩匹配差。作为结论，可以说，尽管SynO不能改善步态期间肌肉激活的预测，但其减小的尺寸控制空间可能对诸如功能性电刺激或运动控制和预测等应用有益。对SO估计的激活进行了协同分析，发现重建的激活产生的实验激活和关节力矩匹配差。作为结论，可以说，尽管SynO不能改善步态期间肌肉激活的预测，但其减小的尺寸控制空间可能对诸如功能性电刺激或运动控制和预测等应用有益。对SO估计的激活进行了协同分析，发现重建的激活产生的实验激活和关节力矩匹配差。作为结论，可以说，尽管SynO不能改善步态期间肌肉激活的预测，但其减小的尺寸控制空间可能对诸如功能性电刺激或运动控制和预测等应用有益。