A predictive model of muscle excitations based on muscle modularity for a large repertoire of human locomotion conditions.
A predictive model of muscle excitations based on muscle modularity for a large repertoire of human locomotion conditions.
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DOI：
10.3389/fncom.2015.00114
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发表时间：
2015
影响因子：
3.2
通讯作者：
Farina D
中科院分区：
文献类型：
--
作者：
Gonzalez-Vargas J;Sartori M;Dosen S;Torricelli D;Pons JL;Farina D
人类只需很少的脑力劳动或无需脑力劳动，就可以有效地穿越各种地形和运动条件。据推测，神经系统通过利用肌肉协同作用来简化神经肌肉控制，从而将多肌肉活动组织成少量的协调共激活模块。在本研究中，我们研究了肌肉模块化是如何在多种运动条件下构建的，包括五种不同的速度和五种不同的地面高度。为此，我们使用非负矩阵分解技术来解释具有四个运动组件的低维集的肌电图实验数据。在这种情况下，每个运动组件都由非负因子和相关的肌肉权重组成。此外，我们还研究了所提出的肌肉模块化描述性分析是否可以转化为预测模型，该模型可以：（1）估计运动部件如何在运动速度和地面高度上进行调节。这意味着不仅要估计非负因素的时间特征，还要估计相关的肌肉权重变化。 (2) 估计由此产生的肌肉兴奋如何在新的运动条件和受试者中进行调节。结果显示了肌肉模块化的三个主要显着特征：（1）在所有运动条件下运动成分的数量都被保留，（2）在所有运动条件下非负面因素在形状和时间上都是一致的，（3）肌肉权重被调节为运动速度和地面高度的独特函数。结果还表明，开发的预测模型能够很好地重现未建模数据（即新的受试者和条件）的肌肉模块性。肌肉权重的重建互相关因子大于 70%，均方根误差小于 0.10。此外，生成的肌肉激励与实验激励匹配良好，互相关因子大于 85%，均方根误差小于 0.09。合成人类在各种运动条件下运动的神经肌肉机制的能力将使神经康复技术和双足人工系统控制领域的解决方案成为可能。 https://simtk.org/home/p-mep/ 提供模型实现的开放访问以供进一步分析。
Humans can efficiently walk across a large variety of terrains and locomotion conditions with little or no mental effort. It has been hypothesized that the nervous system simplifies neuromuscular control by using muscle synergies, thus organizing multi-muscle activity into a small number of coordinative co-activation modules. In the present study we investigated how muscle modularity is structured across a large repertoire of locomotion conditions including five different speeds and five different ground elevations. For this we have used the non-negative matrix factorization technique in order to explain EMG experimental data with a low-dimensional set of four motor components. In this context each motor components is composed of a non-negative factor and the associated muscle weightings. Furthermore, we have investigated if the proposed descriptive analysis of muscle modularity could be translated into a predictive model that could: (1) Estimate how motor components modulate across locomotion speeds and ground elevations. This implies not only estimating the non-negative factors temporal characteristics, but also the associated muscle weighting variations. (2) Estimate how the resulting muscle excitations modulate across novel locomotion conditions and subjects. The results showed three major distinctive features of muscle modularity: (1) the number of motor components was preserved across all locomotion conditions, (2) the non-negative factors were consistent in shape and timing across all locomotion conditions, and (3) the muscle weightings were modulated as distinctive functions of locomotion speed and ground elevation. Results also showed that the developed predictive model was able to reproduce well the muscle modularity of un-modeled data, i.e., novel subjects and conditions. Muscle weightings were reconstructed with a cross-correlation factor greater than 70% and a root mean square error less than 0.10. Furthermore, the generated muscle excitations matched well the experimental excitation with a cross-correlation factor greater than 85% and a root mean square error less than 0.09. The ability of synthetizing the neuromuscular mechanisms underlying human locomotion across a variety of locomotion conditions will enable solutions in the field of neurorehabilitation technologies and control of bipedal artificial systems. Open-access of the model implementation is provided for further analysis at https://simtk.org/home/p-mep/.