EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity.
EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity.
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DOI：
10.1371/journal.pone.0052618
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Lloyd DG
中科院分区：
文献类型：
--
作者：
Sartori M;Reggiani M;Farina D;Lloyd DG
这项工作检验了当前可用的肌电(EMG)驱动模型，当由相同的输入数据驱动，但校准为不同的自由度时，是否可以提供相同的肌腱单位(MTU)力解决方案，这些模型经过校准以满足约一个单自由度的关节力矩。然后，我们开发了一种新颖而全面的人体下肢肌电驱动模型，该模型使用来自16个肌肉群的肌电信号来驱动34个MTU，并满足在不同运动任务中同时产生约4个自由度的关节力矩。这也导致了一种校准程序的开发，该程序允许识别一组特定于受试者的参数，以确保34个MTU的生理行为。结果表明，现有的单自由度模型不能为相同的输入数据提供相同的唯一MTU力解。另一方面，与对应于四个自由度的单自由度模型相比，我们提出的多自由度模型预测的MTU力解满足了多个自由度的关节力矩，而不会损失精度。预测的MTU力解是(1)实验测量肌电的函数，(2)生理MTU激发的结果，(3)反映与不同运动任务相关的不同MTU收缩策略，(4)相对于现有的单自由度模型协调更多的MTU，以及(5)不特定于单个DOF动力学。因此，我们提出的方法有可能产生比单自由度肌电驱动模型更具动态一致性和通用性的MTU力解决方案。这将有助于更好地解决以前使用单自由度肌电驱动建模方法处理的重要科学问题。此外，它可能在辅助设备的人机界面开发中有应用。
This work examined if currently available electromyography (EMG) driven models, that are calibrated to satisfy joint moments about one single degree of freedom (DOF), could provide the same musculotendon unit (MTU) force solution, when driven by the same input data, but calibrated about a different DOF. We then developed a novel and comprehensive EMG-driven model of the human lower extremity that used EMG signals from 16 muscle groups to drive 34 MTUs and satisfy the resulting joint moments simultaneously produced about four DOFs during different motor tasks. This also led to the development of a calibration procedure that allowed identifying a set of subject-specific parameters that ensured physiological behavior for the 34 MTUs. Results showed that currently available single-DOF models did not provide the same unique MTU force solution for the same input data. On the other hand, the MTU force solution predicted by our proposed multi-DOF model satisfied joint moments about multiple DOFs without loss of accuracy compared to single-DOF models corresponding to each of the four DOFs. The predicted MTU force solution was (1) a function of experimentally measured EMGs, (2) the result of physiological MTU excitation, (3) reflected different MTU contraction strategies associated to different motor tasks, (4) coordinated a greater number of MTUs with respect to currently available single-DOF models, and (5) was not specific to an individual DOF dynamics. Therefore, our proposed methodology has the potential of producing a more dynamically consistent and generalizable MTU force solution than was possible using single-DOF EMG-driven models. This will help better address the important scientific questions previously approached using single-DOF EMG-driven modeling. Furthermore, it might have applications in the development of human-machine interfaces for assistive devices.
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