Uncertainty in Limb Configuration Makes Minimal Contribution to Errors Between Observed and Predicted Forces in a Musculoskeletal Model of the Rat Hindlimb.
Uncertainty in Limb Configuration Makes Minimal Contribution to Errors Between Observed and Predicted Forces in a Musculoskeletal Model of the Rat Hindlimb.
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DOI：
10.1109/tbme.2017.2775598
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
Tresch MC
中科院分区：
文献类型：
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作者：
Wei Q;Pai DK;Tresch MC
受试者特定的肌肉骨骼模型越来越多地用于生物医学应用中，以预测由于肌肉激活引起的终点力，将预测的力与在特定肢体配置下实验观察到的力相匹配。然而，很难精确测量观察到这些力的肢体构型。由此产生的不确定性的肢体配置可能会导致错误的模式预测。因此，我们评估了肢体结构测量的不确定性如何导致力预测的误差，使用大鼠后肢的体内测量数据。我们使用数据驱动的方法来估计估计肢体配置的不确定性，然后使用这种配置的不确定性来评估随之而来的不确定性，使用蒙特卡罗模拟的力量预测。我们使用关节结构的受试者特定模型（即旋转中心和旋转轴），以估计每只动物的肢体配置。由配置不确定性引起的预测力方向分布的标准差很小，在肌肉上的范围在0.27和3.05度之间。对于大多数肌肉来说，这个标准差远远小于观察到的力和预测力之间的误差（在0.57和70.96度之间），这表明肢体结构的不确定性不能解释模型预测的不准确性。相反，我们的研究结果表明，肌肉模型参数的不准确性，最有可能在指定肌肉力矩臂的参数，是大鼠后肢肌肉骨骼模型预测误差的主要来源。
Subject-specific musculoskeletal models are increasingly used in biomedical applications to predict endpoint forces due to muscle activation, matching predicted forces to experimentally observed forces at a specific limb configuration. However, it is difficult to precisely measure the limb configuration at which these forces are observed. The consequent uncertainty in limb configuration might contribute to errors in model predictions. We therefore evaluated how uncertainties in limb configuration measurement contributed to errors in force prediction, using data from in vivo measurements in the rat hindlimb. We used a data driven approach to estimate the uncertainty in estimated limb configuration and then used this configuration uncertainty to evaluate the consequent uncertainty in force predictions, using Monte Carlo simulations. We used subject-specific models of joint structures (i.e. centers and axes of rotation) in order to estimate limb configurations for each animal. The standard deviation of the distribution of predicted force directions resulting from configuration uncertainty was small, ranging between 0.27 and 3.05 degrees across muscles. For most muscles, this standard deviation was considerably smaller than the error between observed and predicted forces (between 0.57 and 70.96 degrees), suggesting that uncertainty in limb configuration could not explain inaccuracies in model predictions. Instead, our results suggest that inaccuracies in muscle model parameters, most likely in parameters specifying muscle moment arms, are the main source of prediction errors by musculoskeletal models in the rat hindlimb.