Bueno Diana R, Lizano J M, Montano L
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:494-7. doi: 10.1109/EMBC.2015.7318407.
In this work we have studied different indicators of muscle fatigue from the electrical signal produced by the muscles when contract (sEMG or EMG: surface electromyography): Mean Frequency of the power spectrum (MNF), Median Frequency (Fmed), Dimitrov Spectral Index (FInsm5), Root Mean Square (RMS), and Zerocrossing (ZC). The most reliable features are selected to develop a detection algorithm that estimates muscle fatigue. The approach used in the algorithm is probabilistic and is based on the technique of Gaussian Mixture Model (GMM). The system is divided into two stages: training and validation. During training, the algorithm learns the distribution of data regarding fatigue evolution; after that, the algorithm is validated with data that have not been used to train. Therefore, two experimental sessions have been performed with 6 healthy subjects for biceps.
在这项工作中，我们研究了肌肉收缩时产生的电信号（表面肌电图sEMG或EMG）的不同肌肉疲劳指标：功率谱的平均频率（MNF）、中位数频率（Fmed）、季米特洛夫频谱指数（FInsm5）、均方根（RMS）和过零率（ZC）。选择最可靠的特征来开发一种估计肌肉疲劳的检测算法。该算法采用的方法是概率性的，基于高斯混合模型（GMM）技术。该系统分为两个阶段：训练和验证。在训练期间，算法学习关于疲劳演变的数据分布；之后，用未用于训练的数据对算法进行验证。因此，对6名健康受试者的肱二头肌进行了两次实验。
