Shape model constrained scaling improves repeatability of gait data
Shape model constrained scaling improves repeatability of gait data
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DOI：
10.1016/j.jbiomech.2020.109838
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发表时间：
2020-06-23
影响因子：
2.4
通讯作者：
Besier, Thor
中科院分区：
文献类型：
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作者：
Bakke, Duncan;Besier, Thor
步态研究人员在生成运动学或肌肉骨骼模型时所做的决定是研究人员之间差异的潜在来源，导致可变的模型结果。统计形状模型可以准确地预测骨骼几何形状，并有可能提高临床步态分析的可重复性。本研究的目的是确定使用形状模型来缩放节段长度和关节中心位置与线性缩放方法相比是否会提高运动学和动力学步态数据的可重复性。五名参与者完成了一项动作捕捉实验，包括站立静态试验和以自选速度行走。来自静态试验的解剖标志由五位经验丰富的研究人员使用两种方法生成运动学模型：（1）OpenSim中的线性缩放，以及（2）使用我们的“MAP客户端”缩放工具的形状模型缩放。由此产生的模型被用来执行行走试验的逆运动学和逆动力学分析，研究人员之间的变化进行了分析，通过比较使用不同的模型从相同的动作捕捉试验的输出。与形状模型缩放相比，使用线性缩放时，研究者之间在关节角度（P < 0.001）、关节力矩（P < 0.005）和关节功率（P < 0.005）方面观察到更高的变异性。与形状模型比例模型相比，线性比例模型的变异至少是其三倍。我们已经发现，即使使用相同的实验数据，线性缩放也会导致研究人员之间步态数据的显著差异。使用形状模型来缩放肌肉骨骼模型可以产生可重复的运动学和动力学步态数据。(C)2020爱思唯尔有限公司保留所有权利。
Decisions made by gait researchers in the generation of kinematic or musculoskeletal models are a potential source of variation between researchers, leading to variable model outcomes. Statistical shape models can accurately predict bone geometry and have the potential to improve the repeatability of clinical gait analysis. The purpose of this study was to determine if using a shape model to scale segment length and joint centre locations would improve repeatability of kinematic and kinetic gait data, compared to linear scaling methods. Five participants completed a motion capture experiment, including a standing static trial and walking at a self-selected speed. Anatomical landmarks from the static trial were used by five experienced researchers to generate kinematic models using two methods; (1) linear scaling in OpenSim, and (2) shape-model scaling using our 'MAP Client' scale tool. The resulting models were used to perform an inverse kinematic and inverse dynamic analysis on the walking trials, and variation between researchers was analysed by comparing outputs from the same motion capture trial using different models. Higher variability between researchers was observed in joint angles (P < 0.001), joint moments (P < 0.005), and joint powers (P < 0.005) when using linear scaling, compared to shape-model scaling. Variation was at least three times as large for linearly-scaled models compared to shape-model scaled models. We have identified that linear scaling can lead to substantial variability in gait data across researchers, even with the same experimental data. Using a shape model to scale musculoskeletal models results in repeatable kinematic and kinetic gait data. (C) 2020 Elsevier Ltd. All rights reserved.