目的:大约二十年前,随着运动命令的产生作为肌肉协同作用的组合的模型的引入,在运动控制研究中开辟了一个新的方向。肌肉协同作用提供了一个简单而定量的框架,用于分析人类运动系统的分层和模块化体系结构。然而,为了深入了解肌肉协同作用的功能作用,它们应该与任务空间相关。最近引入的混合矩阵分解(MMF)算法扩展了基于非负矩阵分解(NMF)的协同提取标准方法,允许对由非负变量(例如EMG)和无约束变量(例如运动学,自然包括正值和负值)。MMF确定的运动肌肉协同作用提供了肌肉协同作用和任务空间之间的直接联系。在这一贡献中,我们支持通过Matlab工具箱采用MMF来提取运动-肌肉协同作用,并提供一套实用指南,使生物医学研究人员和临床医生能够利用这种新方法的潜力.
方法:MMF在SynergyAnalyzer工具箱中使用面向对象的方法实现。除了MMF算法,工具箱包括协同提取的标准方法(NMF和PCA),以及预处理EMG和运动学数据的方法,以及绘制数据和协同效应。
结果:作为MMF应用程序的示例,从EMG和在向矢状平面上的8个目标到达运动过程中收集的运动学数据中提取了运动学-肌肉协同作用。详细说明了实现这些结果的指令和命令行。该工具箱已在GitHub上根据GNU通用公共许可证作为开源软件发布。
结论:由于其易用性和对各种数据集的适应性,SynergyAnalyzer将促进MMF的采用,以从混合的EMG和运动学数据中提取运动肌肉协同作用,在生物医学研究中一个有用的方法,以更好地理解和表征肌肉协同作用的功能作用。
OBJECTIVE: A new direction in the study of motor control was opened about two decades ago with the introduction of a model for the generation of motor commands as combination of muscle synergies. Muscle synergies provide a simple yet quantitative framework for analyzing the hierarchical and modular architecture of the human motor system. However, to gain insights on the functional role of muscle synergies, they should be related to the task space. The recently introduced mixed-matrix factorization (MMF) algorithm extends the standard approach for synergy extraction based on non-negative matrix factorization (NMF) allowing to factorize data constituted by a mixture of non-negative variables (e.g. EMGs) and unconstrained variables (e.g. kinematics, naturally including both positive and negative values). The kinematic-muscular synergies identified by MMF provide a direct link between muscle synergies and the task space. In this contribution, we support the adoption of MMF through a Matlab toolbox for the extraction of kinematic-muscular synergies and a set of practical guidelines to allow biomedical researchers and clinicians to exploit the potential of this novel approach.
METHODS: MMF is implemented in the SynergyAnalyzer toolbox using an object-oriented approach. In addition to the MMF algorithm, the toolbox includes standard methods for synergy extraction (NMF and PCA), as well as methods for pre-processing EMG and kinematic data, and for plotting data and synergies.
RESULTS: As an example of MMF application, kinematic-muscular synergies were extracted from EMG and kinematic data collected during reaching movements towards 8 targets on the sagittal plane. Instructions and command lines to achieve such results are illustrated in detail. The toolbox has been released as an open-source software on GitHub under the GNU General Public License.
CONCLUSIONS: Thanks to its ease of use and adaptability to a variety of datasets, SynergyAnalyzer will facilitate the adoption of MMF to extract kinematic-muscular synergies from mixed EMG and kinematic data, a useful approach in biomedical research to better understand and characterize the functional role of muscle synergies.