Muscle synergies may improve optimization prediction of knee contact forces during walking.
Muscle synergies may improve optimization prediction of knee contact forces during walking.
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DOI：
10.1115/1.4026428
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发表时间：
2014-02
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通讯作者：
Jonathan P. Walter;Allison L. Kinney;S. Banks;D. D’Lima;T. Besier;D. Lloyd;B. Fregly
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文献类型：
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作者：
Jonathan P. Walter;Allison L. Kinney;S. Banks;D. D’Lima;T. Besier;D. Lloyd;B. Fregly
准确预测患者特定关节接触和肌肉力量的能力可以改善步行相关疾病的治疗。肌肉协同分析将大量的肌肉肌电图信号分解为少量的协同控制信号，可以减少肌肉和接触力预测过程的维数和冗余。本研究调查是否使用特定主题的协同控制，可以提高步行过程中的膝盖接触力的优化预测。为了生成预测，我们进行了混合动态肌肉力量优化（即，具有正向肌肉激活和收缩动力学的反向骨骼动力学）。十二个优化问题（三种情况下，每个四个子情况下），最大限度地减少肌肉激励的平方和制定调查如何协同控制影响膝关节接触力的预测。这三起案件是：（1）校准+匹配，其中肌肉模型参数值被校准并且实验膝盖接触力被同时匹配，（2）预校准+预测，其中使用来自第一种情况的预校准的肌肉模型参数值来预测实验膝盖接触力，以及（3）校准+预测，其中肌肉模型参数值被校准并且实验膝盖接触力被同时预测，同时匹配髋关节、膝关节和踝关节处的反向动态载荷。这四个子案例使用了44个独立控制或5个协同控制，有或没有EMG形状跟踪。对于校准+匹配情况，所有四个子情况均接近地再现了测量的内侧和外侧膝关节接触力（R2 ≥ 0.94，均方根（RMS）误差< 66 N），表明接触力预测的模型保真度足够。对于预校准+预测和校准+预测情况，协同控制产生的接触力预测（0.61 < R2 < 0.90，83 N < RMS误差< 161 N）优于相应子情况的独立控制（-0.15 < R2 < 0.79，124 N < RMS误差< 343 N）。对于独立控制，当使用预校准模型参数值或EMG形状跟踪时，接触力预测得到改善。对于协同控制，接触力预测对模型参数值如何校准相对不敏感，而EMG形状跟踪使外侧（但不是内侧）接触力预测变差。对于本研究中分析的主题和优化成本函数，使用特定主题的协同控制提高了膝关节接触力预测的准确性，特别是当省略EMG形状跟踪时的横向接触力，并降低了对肌肉模型参数值的不确定性的预测灵敏度。
The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values.