Practical approach to subject-specific estimation of knee joint contact force.
Practical approach to subject-specific estimation of knee joint contact force.
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DOI：
10.1016/j.jbiomech.2015.04.020
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发表时间：
2015-08-20
影响因子：
2.4
通讯作者：
Higginson JS
中科院分区：
文献类型：
--
作者：
Knarr BA;Higginson JS
膝盖承受的压力会显着导致软骨退化。肌肉骨骼模型可以预测膝盖所经历的内力，但通常无法进行验证，因为详细说明膝关节负载的实验数据有限。最近获得的数据通过使用仪器化全膝关节置换术直接测量来报告膝关节压缩力，为评估模型的准确性提供了独特的机会。先前的研究强调了主题特异性在提高模型预测准确性方面的重要性；然而，这些技术在研究环境之外可能不切实际。因此，我们工作的目标是找到一种准确预测胫股膝关节接触力（KCF）的实用方法。比较了四种预测膝关节接触力的方法：（1）标准静态优化，（2）统一肌肉协调权重，（3）特定于受试者的肌肉协调权重和（4）特定于受试者的力量调整。对三名使用仪器膝关节置换术的受试者进行步行试验，用于评估模型预测的准确性。利用受试者特定的肌肉协调权重进行的预测与实验数据达到了最佳一致性，但是该方法需要体内数据来进行权重因子校准。与标准静态优化相比，包括特定于受试者的力量调整改进了模型的预测，所有受试者的峰值 KCF 误差小于 0.5 体重。总体而言，将肌肉力量的临床评估与 OpenSim 软件包中提供的标准工具（例如逆运动学和静态优化）相结合，似乎是预测关节接触力的实用方法，可在许多应用中实施。
Compressive forces experienced at the knee can significantly contribute to cartilage degeneration. Musculoskeletal models enable predictions of the internal forces experienced at the knee, but validation is often not possible, as experimental data detailing loading at the knee joint is limited. Recently available data reporting compressive knee force through direct measurement using instrumented total knee replacements offer a unique opportunity to evaluate the accuracy of models. Previous studies have highlighted the importance of subject-specificity in increasing the accuracy of model predictions; however, these techniques may be unrealistic outside of a research setting. Therefore, the goal of our work was to identify a practical approach for accurate prediction of tibiofemoral knee contact force (KCF). Four methods for prediction of knee contact force were compared: (1) standard static optimization, (2) uniform muscle coordination weighting, (3) subject-specific muscle coordination weighting and (4) subject-specific strength adjustments. Walking trials for three subjects with instrumented knee replacements were used to evaluate the accuracy of model predictions. Predictions utilizing subject-specific muscle coordination weighting yielded the best agreement with experimental data, however this method required in vivo data for weighting factor calibration. Including subject-specific strength adjustments improved models’ predictions compared to standard static optimization, with errors in peak KCF less than 0.5 body weight for all subjects. Overall, combining clinical assessments of muscle strength with standard tools available in the OpenSim software package, such as inverse kinematics and static optimization, appears to be a practical method for predicting joint contact force that can be implemented for many applications.