Centre for Sport Science and University Sports, Department of Biomechanics, Kinesiology and Computer Science in Sport, Neuromechanics Research Group, University of Vienna, Austria.
Human Movement Biomechanics Research Group, KU Leuven, Belgium; Faculty of Mechanical, Maritime and Materials Engineering, Department of Biomechanical Engineering, Biomechatronics and Human-Machine Control, TU Delft, Netherlands.
Clin Biomech (Bristol). 2021 Jul;87:105402. doi: 10.1016/j.clinbiomech.2021.105402. Epub 2021 Jun 1.
Musculoskeletal modelling is used to assess musculoskeletal loading during gait. Linear scaling methods are used to personalize generic models to each participant's anthropometry. This approach introduces simplifications, especially when used in paediatric and/or pathological populations. This study aimed to compare results from musculoskeletal simulations using various models ranging from linear scaled to highly subject-specific models, i.e., including the participant's musculoskeletal geometry and electromyography data.
Magnetic resonance images (MRI) and gait data of one typically developing child and three children with cerebral palsy were analysed. Musculoskeletal simulations were performed to calculate joint kinematics, joint kinetics, muscle forces and joint contact forces using four modelling frameworks: 1) Generic-scaled model with static optimization, 2) Generic-scaled model with an electromyography-informed approach, 3) MRI-based model with static optimization, and 4) MRI-based model with an electromyography-informed approach.
Root-mean-square-differences in joint kinematics and kinetics between generic-scaled and MRI-based models were below 5° and 0.15 Nm/kg, respectively. Root-mean-square-differences over all muscles was below 0.2 body weight for every participant. Root-mean-square-differences in joint contact forces between the different modelling frameworks were up to 2.2 body weight. Comparing the simulation results from the typically developing child with the results from the children with cerebral palsy showed similar root-mean-square-differences for all modelling frameworks.
In our participants, the impact of MRI-based models on joint contact forces was higher than the impact of including electromyography. Clinical reasoning based on overall root-mean-square-differences in musculoskeletal simulation results between healthy and pathological participants are unlikely to be affected by the modelling choice.
肌肉骨骼建模用于评估步态过程中的肌肉骨骼负荷。线性缩放方法用于将通用模型个性化为每个参与者的人体测量学。这种方法引入了简化,尤其是在儿科和/或病理人群中使用时。本研究旨在比较使用各种模型(从线性缩放模型到高度特定于个体的模型)进行肌肉骨骼模拟的结果,即包括参与者的肌肉骨骼几何形状和肌电图数据。
分析了一名正常发育儿童和三名脑瘫儿童的磁共振成像(MRI)和步态数据。使用四种建模框架进行肌肉骨骼模拟,以计算关节运动学、关节动力学、肌肉力量和关节接触力:1)具有静态优化的通用缩放模型,2)具有肌电图信息的通用缩放模型,3)具有静态优化的基于 MRI 的模型,以及 4)具有肌电图信息的基于 MRI 的模型。
通用缩放模型和基于 MRI 的模型之间的关节运动学和动力学的均方根差异分别低于 5°和 0.15 Nm/kg。每个参与者的所有肌肉的均方根差异均低于 0.2 体重。不同建模框架之间的关节接触力的均方根差异高达 2.2 体重。将正常发育儿童的模拟结果与脑瘫儿童的结果进行比较,所有建模框架的均方根差异均相似。
在我们的参与者中,基于 MRI 的模型对关节接触力的影响高于包括肌电图的影响。基于健康和病理参与者之间肌肉骨骼模拟结果的整体均方根差异进行临床推理不太可能受到建模选择的影响。