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一种用于马蹄内翻足步态中肌腱力估计的跟踪肌电图、地面反作用力和标记轨迹的优化方法。

An Optimization Method Tracking EMG, Ground Reactions Forces, and Marker Trajectories for Musculo-Tendon Forces Estimation in Equinus Gait.

作者信息

Moissenet Florent, Bélaise Colombe, Piche Elodie, Michaud Benjamin, Begon Mickaël

机构信息

Centre National de Rééducation Fonctionnelle et de Réadaptation-Rehazenter, Luxembourg, Luxembourg.

Laboratory of Simulation and Movement Modeling, School of Kinesiology and Exercise Sciences, Université de Montréal, Montreal, QC, Canada.

出版信息

Front Neurorobot. 2019 Jul 16;13:48. doi: 10.3389/fnbot.2019.00048. eCollection 2019.

Abstract

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.

摘要

在影响行走进而影响患者生活质量的神经骨科疾病背景下,了解步态偏差的机制并识别因果性运动障碍至关重要。除其他方法外,神经肌肉骨骼模拟可用于深入了解这一问题。据我们所知,文献中不存在能够进行具有肌肉共同收缩以及在健康和病理步态类型中引入各种类型扰动的预测性模拟的计算框架。这项初步研究的目的是将最近提出的肌电图 - 标记跟踪优化过程应用于马蹄内翻足步态期间的下肢肌肉骨骼模型,这是一个涉及接触力的多阶段问题。由此产生的跟踪肌电图、地面反作用力和标记轨迹的优化方法能够准确再现关节运动学(骨盆平移的平均误差为5.4±3.3毫米,骨盆旋转和关节角度的平均误差为1.9±1.3°),并在相对较快的过程(3.88±1.04小时)中确保肌肉活动具有良好的时间一致性(估计和测量的兴奋之间的一致性为76.8±5.3%)。我们还强调,即使不使用复杂的足底 - 地面接触模型,也能够准确跟踪地面反作用力(平均误差为17.3±5.5牛)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9320/6646662/1f2bb67dfaab/fnbot-13-00048-g0001.jpg

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