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.

步态研究者在运动学模型或肌肉骨骼模型的生成中做出的决定是研究者之间差异的潜在来源，从而导致模型结果可变。统计形状模型可以准确地预测骨骼的几何形状，并具有改善临床步态分析可重复性的潜力。这项研究的目的是确定与线性缩放方法相比，使用形状模型缩放路段长度和关节中心位置是否会改善运动和运动步态数据的可重复性。五名参与者完成了动作捕捉实验，包括站立静态试验和以自选速度行走。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）在OpenSim中进行线性缩放，（2）使用我们的“ MAP Client”缩放工具进行形状模型缩放。所得模型用于对步行试验进行运动学和动力学的逆分析，并通过比较使用不同模型的同一运动捕捉试验的输出来分析研究人员之间的差异。与形状模型缩放相比，使用线性缩放时，研究人员之间的关节角度（P <0.001），关节力矩（P <0.005）和关节屈光力（P <0.005）的变异性更高。与形状模型缩放模型相比，线性缩放模型的变化至少大三倍。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的步态数据发生较大变化。