A predictive model of muscle excitations based on muscle modularity

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. With this project we want to investigate 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. This descriptive analysis was 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. <br/><br/>This project includes the following software/data packages: <br/> <ul> <li> <a href="https://simtk.org/frs?group_id=1031#pack_1794">MEP_class </a> : A C++ class can be accessed through the git repository: https://[e-mail]/jegonz/mep_class.gitAlso a demo of how to use the class can be found at: https://[e-mail]/jegonz/mep_demo.git </li> </ul>

人类能够在众多地形和运动条件下，凭借微乎其微的精神努力高效行走。据此假设，神经系统通过运用肌肉协同作用简化了神经肌肉控制，从而将多肌肉活动组织成少数协调共激活模块。本项研究旨在探讨肌肉模块在众多运动条件下的结构，包括五种不同的速度和五种不同的地面高度。为此，我们采用了非负矩阵分解技术，以解释低维度的四组运动成分所对应的肌电图实验数据。该描述性分析被转化为预测模型，能够：1）估计运动成分如何调节运动速度和地面高度。这不仅意味着估计非负因子的时序特性，还包括相关的肌肉权重变化。2）估计由此产生的肌肉兴奋如何调节新型运动条件和不同个体。本项研究涉及以下软件/数据包：
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<li><a href="https://simtk.org/frs?group_id=1031#pack_1794">MEP_class</a>：一个可通过git仓库访问的C++类，仓库地址：https://[e-mail]/jegonz/mep_class.git。此外，有关如何使用该类的演示可在以下地址找到：https://[e-mail]/jegonz/mep_demo.git</li>
</ul>