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Neuromusculoskeletal model calibration significantly affects predicted knee contact forces for walking

机译:神经肌肉骨骼模型校准显着影响预测的行走膝关节力

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摘要

© 2016 by ASME. Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r=0.99 and root mean square error (RMSE)=52.6N medial; average r=0.95 and RMSE=56.6N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE=323 N medial and 348N lateral) and poorly matched contact force shape for the lateral compartment (average r=0.90 medial and-0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal muscle fiber lengths but lower medial, central, and lateral normalized muscle fiber lengths compared to Approach A. These findings suggest that poorly calibrated model parameter values may be a major factor limiting the ability of neuromusculoskeletal models to predict knee contact and leg muscle forces accurately for walking.
机译:©2016,ASME。尽管步行障碍在社会上很普遍,但临床治疗通常无法恢复失去的功能。因此,研究人员已开始探索使用针对特定患者的计算步行模型来开发更有效的治疗方法。但是,模型可以预测肌肉和跨关节内部力量的准确度取决于可以为患者校准的相关模型参数值的准确性。这项研究调查了内部膝盖接触力的知识如何影响神经肌肉骨骼模型参数值的校准以及步行过程中内部膝盖接触和腿部肌肉力的后续预测。使用新颖的两级优化程序进行模型校准,该程序应用于第四次大型挑战赛的六项正常步行试验,以预测体内膝关节负荷。外层优化调整了时不变模型参数值,以最大程度地减小被动肌肉力,储备执行器力矩以及在(方法A)和(方法B)没有跟踪实验膝部接触力的情况下模型参数值的变化。使用当前对模型参数值的猜测但没有膝盖接触力信息,内部层级优化预测了随实验肌肉协同作用模式变化并与实验逆动态负荷一致的时变肌肉激活(两种方法)。对于所有六个步态试验,方法A预测两个舱室的膝关节接触力的准确性较高(平均相关系数r = 0.99和均方根误差(RMSE)= 52.6N内侧;平均r = 0.95,RMSE = 56.6N外侧) 。相比之下,方法B高估了两个隔室的接触力大小(平均RMSE = 323 N内侧和348N外侧),而接触力形状的匹配不佳(平均r = 0.90内侧和-0.10外侧)。与方法A相比,方法B具有统计学上更高的外侧肌肉力量和外侧最佳肌肉纤维长度,但内侧,中部和外侧标准化肌肉纤维长度较低。这些发现表明,标定模型参数值较差可能是限制神经肌肉骨骼能力的主要因素这些模型可以准确预测步行时的膝盖接触和腿部肌肉力量。

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