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首页> 外文会议>International Symposium on Medical Measurements and Applications >The effect of Non-Negative Matrix Factorization initialization on the accurate identification of muscle synergies with correlated activation signals
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The effect of Non-Negative Matrix Factorization initialization on the accurate identification of muscle synergies with correlated activation signals

机译：非负矩阵分解初始化对具有相关激活信号的肌肉协同作用的准确识别的作用

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The quantification of muscle coordination through muscle synergy analysis has been shown to represent a useful tool for inferring motor control strategies and extracting indexes of motor impairment and recovery. The main goal of this work has been the assessment of the performance of different initializations techniques for Nonnegative Matrix Factorization (NNMF) for the extraction of muscle synergies. Previous research has shown that NNMF performance might be affected by different kinds of initialization. The present study aims at optimizing the traditional NNMF initialization for decomposing EMG data with temporal dependencies, typical of pathological conditions such as stroke. For this purpose, three different initializations have been implemented: random, SVD-based, and sparse. Traditional NNMF update rules have been used to identify muscle synergies from experimental EMG signals recorded during pedaling from 11 subjects. Synthetic data have been generated from real EMG data, whose activation coefficients have been corrupted by simulating different degrees of correlation. By measuring the quality of identification of the original synergies underlying the data it has been possible to compare the performance of different initialization techniques. Simulation results demonstrate that sparse initialization performs significantly better than all the other kinds of initialization in accurately estimating muscle synergies when the activation coefficients are characterized by high levels of correlation.
机译：通过肌肉协同分析对肌肉协调性进行量化已显示出可用于推断运动控制策略并提取运动障碍和恢复指标的有用工具。这项工作的主要目标是评估用于提取肌肉协同作用的非负矩阵分解（NNMF）的不同初始化技术的性能。先前的研究表明，NNMF性能可能会受到不同类型的初始化的影响。本研究旨在优化传统的NNMF初始化，以分解具有时间依赖性的EMG数据，这些时间依赖性是典型的病理条件（例如中风）。为此，已实现了三种不同的初始化：随机，基于SVD的和稀疏的。传统的NNMF更新规则已被用来从11名受试者的踩踏过程中记录的实验EMG信号中识别肌肉协同作用。已从实际的EMG数据生成了合成数据，其真实的激活系数已通过模拟不同的相关程度而被破坏。通过测量数据背后原始协同作用的识别质量，可以比较不同初始化技术的性能。仿真结果表明，当激活系数具有较高的相关性时，稀疏初始化的性能要比所有其他类型的初始化好得多，可以准确地估计肌肉的协同作用。

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