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协同分析器:一个Matlab工具箱,实现混合矩阵分解,以确定动力-肌肉协同作用

Marta Russo¹, Alessandro Scano², Cristina Brambilla²

¹Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, Italy; Deparment of Neurology, Fondazione Policlinico Tor Vergata, Rome, Italy.

Computer methods and programs in biomedicine

|May 14, 2024

概括

这项研究引入了一个新的工具箱,通过将它们与运动联系起来来分析肌肉协同作用. 协同分析器通过结合肌肉和运动数据来促进理解运动控制.

科学领域:

生物医学工程生物医学工程
神经科学是一个神经科学.
生物力学生物力学

背景情况:

肌肉协同作用是理解运动控制和人类运动系统架构的关键.
将肌肉协同作用与任务空间联系起来对于功能洞察至关重要.
像NMF这样的现有方法在混合数据类型方面存在局限性.

研究的目的:

介绍一种新的方法来利用混合矩阵因子化 (MMF) 来提取动力学-肌肉协同作用.
提供一个实用的Matlab工具箱 (SynergyAnalyzer) 来实现MMF.
为研究人员和临床医生提供使用MMF分析运动控制的指导方针.

主要方法:

在SynergyAnalyzer工具箱中使用面向对象设计实现了MMF.
包括标准的协同提取方法 (NMF,PCA) 和数据预处理工具.
开发了数据的绘图功能,并提取了协同效应.

主要成果:

通过从EMG和运动数据中提取运动肌肉协同效应来证明MMF的应用.
提供详细的指令和命令行来复制结果.
在GitHub上发布了SynergyAnalyzer工具箱作为开源软件.

结论:

协同分析器工具箱简化了MMF的采用,以提取动力-肌肉协同效应.
MMF为生物医学研究提供了一种有价值的方法,以了解肌肉协同作用的功能作用.
使用方便和适应性使SynergyAnalyzer对各种数据集有益.

关键词:

功能上的协同效应.运动 - 肌肉协同作用.马特拉布的工具箱就是一个工具箱.混合矩阵分解因子的分解.肌肉的协同作用.非负矩阵因数分解的非负矩阵因数分解

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