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肌动图(MMG)记录了肌纤维的低频侧向振动,表征了肌肉活动的力学特性。MMG信号可用于估计肌肉疲劳。希尔伯特-黄变换(HHT)作为一种时频分析方法,具有自适应性,适合于非线性、非平稳信号分析。本文利用HHT对上肢肱二头肌等长收缩疲劳实验中记录的MMG信号进行分析,提取计算瞬时频率的最高值与最低值的差(频带值)作为特征量,以估计肌肉疲劳特性。实验结果表明,当肌肉完全疲劳以后,50%最大力矩值和70%最大力矩值情况下,频带比分别为0.431±0.607和0.286±0.218,说明肌肉疲劳以后,频率有了明显地下降。
Muscle mapping (MMG) documents low-frequency lateral vibration of muscle fibers, characterizing the mechanical properties of muscle activity. The MMG signal can be used to estimate muscle fatigue. Hilbert-Huang Transform (HHT) as a time-frequency analysis method, adaptive, suitable for nonlinear, non-stationary signal analysis. In this paper, the MMG signal recorded in isometric fatigue test of upper biceps brachial muscle was analyzed by HHT, and the difference between the highest value and the lowest value of the instantaneous frequency (frequency band value) was extracted as the feature quantity to estimate the muscle fatigue characteristics. The experimental results show that when the muscle is completely exhausted, the frequency ratios are 0.431 ± 0.607 and 0.286 ± 0.218, respectively, when the maximal torque value is 50% and the max torque value is 70%, which indicates that the frequency has obviously decreased after muscle fatigue.