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首页> 美国卫生研究院文献>Frontiers in Neurorobotics >An Optimization Method Tracking EMG Ground Reactions Forces and Marker Trajectories for Musculo-Tendon Forces Estimation in Equinus Gait
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An Optimization Method Tracking EMG Ground Reactions Forces and Marker Trajectories for Musculo-Tendon Forces Estimation in Equinus Gait

机译：跟踪肌电图地面反作用力和标记轨迹的优化方法用于估计马匹步态中的肌腱力

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In the context of neuro-orthopedic pathologies affecting walking and thus patients' quality of life, understanding the mechanisms of gait deviations and identifying the causal motor impairments is of primary importance. Beside other approaches, neuromusculoskeletal simulations may be used to provide insight into this matter. To the best of our knowledge, no computational framework exists in the literature that allows for predictive simulations featuring muscle co-contractions, and the introduction of various types of perturbations during both healthy and pathological gait types. The aim of this preliminary study was to adapt a recently proposed EMG-marker tracking optimization process to a lower limb musculoskeletal model during equinus gait, a multiphase problem with contact forces. The resulting optimization method tracking EMG, ground reactions forces, and marker trajectories allowed an accurate reproduction of joint kinematics (average error of 5.4 ± 3.3 mm for pelvis translations, and 1.9 ± 1.3° for pelvis rotation and joint angles) and ensured good temporal agreement in muscle activity (the concordance between estimated and measured excitations was 76.8 ± 5.3 %) in a relatively fast process (3.88 ± 1.04 h). We have also highlighted that the tracking of ground reaction forces was possible and accurate (average error of 17.3 ± 5.5 N), even without the use of a complex foot-ground contact model.
机译：在影响步行并进而影响患者生活质量的神经骨科病理学的背景下，了解步态偏离的机制并确定因果运动障碍是最重要的。除其他方法外，神经肌肉骨骼模拟可用于提供对此问题的见解。据我们所知，文献中没有任何计算框架可以进行以肌肉共收缩为特征的预测模拟，并在健康和病理性步态类型中引入各种类型的摄动。这项初步研究的目的是使最近提出的EMG标记跟踪优化过程适应马蹄形步态（一种具有接触力的多相问题）下肢肌肉骨骼模型。跟踪肌电图，地面反作用力和标记轨迹的优化方法可以准确再现关节运动学（骨盆平移的平均误差为5.4±3.3 mm，骨盆旋转和关节角度的平均误差为1.9±1.3°），并确保了良好的时间一致性在一个相对较快的过程中（3.88±1.04小时），肌肉活动（估计的激励与测量的激励之间的一致性为76.8±5.3％）。我们还强调指出，即使不使用复杂的脚底接触模型，也可以跟踪地面反作用力并进行准确的跟踪（平均误差为17.3±5.5 N）。

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