Background

To be able to use muscluloskeletal models in clinical settings, it is important to understand the effect of walking speed on joint and muscle force estimations in different generic musculoskeletal models.

Research question

The aim of the current study is to compare estimated joint and muscle forces as a function of walking speed between two standard approaches offered in two different modelling environments (AnyBody and OpenSim).

Methods

Experimental data of 10 healthy participants were recorded at three different walking speeds (self-selected, 25 % slower, 25 % faster) using a ten-camera motion capture system together with four force plates embedded into a ten-meter walkway. Joint compression forces and muscle forces were calculated with a generic model in AnyBody and OpenSim. Trend analyses, mean absolute error (MAE) and correlation coefficients were used to compare joint compression forces and muscle forces between the two approaches. A one-way and two-way ANOVA with repeated measures were used to compare MAE and trend analysis changes, respectively (α = 0.05, Bonferroni corrected post-hoc tests).

Results

Trend analyses showed the same speed effect for AnyBody and OpenSim. MAEs increased significantly from slow to fast walking for knee joint compression forces, biceps femoris long head, gluteus maximus, gluteus medius and vastus intermedius. Lower correlation coefficients during slower walking were found for quadriceps muscles, gluteus maximus and biceps femoris compared to normal and faster walking.

Significance

Lower correlation coefficients during slower walking are assumed to be due to a higher amount of solutions solving the muscle recruitment in musculoskeletal models. This indicates that decreasing walking speed is more prone to speed dependent differences regarding variability, while the absolute error increased with increasing walking speed. To conclude, different modelling environments can react differently to changes in walking speed, but overall results are promising regarding the generalization across different generic musculoskeletal models.

中文翻译：

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使用 AnyBody 和 OpenSim 评估不同步行速度对关节和肌肉力的影响

背景

为了能够在临床环境中使用肌肉骨骼模型，了解步行速度对不同通用肌肉骨骼模型中关节和肌肉力量估计的影响非常重要。

研究问题

当前研究的目的是在两种不同建模环境（AnyBody 和 OpenSim）中提供的两种标准方法之间比较估计的关节和肌肉力量作为步行速度的函数。

方法

10 名健康参与者的实验数据以三种不同的步行速度（自行选择，慢 25%，快 25%）使用十相机运动捕捉系统和四个嵌入十米人行道的测力板记录。使用 AnyBody 和 OpenSim 中的通用模型计算关节压缩力和肌肉力。趋势分析、平均绝对误差 (MAE) 和相关系数用于比较两种方法之间的关节压缩力和肌肉力。具有重复测量的单向和双向方差分析分别用于比较 MAE 和趋势分析变化（α = 0.05，Bonferroni 校正事后检验）。

结果

趋势分析显示 AnyBody 和 OpenSim 的速度效果相同。对于膝关节压缩力、股二头肌长头、臀大肌、臀中肌和股中间肌，MAE从慢走到快走显着增加。与正常和快速步行相比，在较慢步行过程中，股四头肌、臀大肌和股二头肌的相关系数较低。

意义

在较慢的步行过程中较低的相关系数被认为是由于解决肌肉骨骼模型中肌肉募集的解决方案数量较多。这表明降低步行速度更容易产生关于可变性的速度依赖差异，而绝对误差随着步行速度的增加而增加。总而言之，不同的建模环境可以对步行速度的变化做出不同的反应，但关于跨不同通用肌肉骨骼模型的泛化的总体结果是有希望的。