一种基于sEMG信号多重分形的肌肉疲劳特征分析方法

针对由表面肌电信号(sEMG)非平稳、非线性、自相似性等复杂特性导致的肌肉疲劳估计不准的问题，提出一种基于sEMG信号多重分形降趋移动平均法(MFDMA)的肌肉疲劳特征分析方法。首先，利用MFDMA方法对采集的sEMG信号、洗牌信号和高斯白噪声信号进行非线性动力学分析；其次，利用MFDMA方法计算sEMG信号的多重分形谱宽度、Hurst指数变化差值、概率测度值和峰值奇异指数4种多重分形特征；最后，利用t-检验法分析肌肉疲劳与非疲劳状态下的多重分形特征的显著差异性。结果表明，MFDMA方法能够描述sEMG信号的多重分形行为，谱宽等多重分形特征在肌肉疲劳与非疲劳状态下具有显著性差异。所提方法能够可靠表征运动性肌肉疲劳，可为肌肉疲劳识别模型建构、康复医学研究提供特征参考。

A method for analyzing muscle fatigue characteristics based on sEMG signal multifractal

Aiming at the problem that surface EMG signals (sEMG) are inaccurate in estimating muscle fatigue due to their non-stationary, nonlinear, self-similarity and other complex characteristics, a method for analyzing muscle fatigue characteristics based on sEMG signal multifractal downtrend moving average method (MFDMA) was proposed. Firstly, the MFDMA method was used to analyze the nonlinear dynamics of the collected sEMG signal, shuffle signal and Gaussian white noise signal; secondly, MFDMA method was used to calculate the multifractal spectrum width, Hurst exponent variation difference, probability measure value and peak singularity exponent of sEMG signal; finally, the significant difference in multifractal characteristics between muscle fatigue and non-fatigue state was analyzed by t-test. The results show that MFDMA method can describe the multifractal behavior of sEMG signal, and the multifractal characteristics such as spectral width have significant differences between muscle fatigue and non-fatigue state. The proposed method can reliably characterize exercise-induced muscle fatigue, and provide some feature reference for muscle fatigue recognition model and rehabilitation medicine research.