Department of Industrial, Electronics and Mechanical Engineering (DIIEM), Roma Tre University, 00146 Rome, Italy.
Sensors (Basel). 2022 Aug 24;22(17):6360. doi: 10.3390/s22176360.
Mean and Median frequency are typically used for detecting and monitoring muscle fatigue. These parameters are extracted from power spectral density whose estimate can be obtained by several techniques, each one characterized by advantages and disadvantages. Previous works studied how the implementation settings can influence the performance of these techniques; nevertheless, the estimation results have never been fully evaluated when the power density spectrum is in a low-frequency zone, as happens to the surface electromyography (sEMG) spectrum during muscle fatigue. The latter is therefore the objective of this study that has compared the Welch and the autoregressive parametric approaches on synthetic sEMG signals simulating severe muscle fatigue. Moreover, the sensitivity of both the approaches to the observation duration and to the level of noise has been analyzed. Results showed that the mean frequency greatly depends on the noise level, and that for Signal to Noise Ratio (SNR) less than 10dB the errors make the estimate unacceptable. On the other hand, the error in calculating the median frequency is always in the range 2-10 Hz, so this parameter should be preferred in the tracking of muscle fatigue. Results show that the autoregressive model always outperforms the Welch technique, and that the 3rd order continuously produced accurate and precise estimates; consequently, the latter should be used when analyzing severe fatiguing contraction.
均值和中位数频率通常用于检测和监测肌肉疲劳。这些参数是从功率谱密度中提取出来的，其估计可以通过几种技术获得，每种技术都有其优缺点。以前的研究研究了实施设置如何影响这些技术的性能；然而，当功率密度谱处于低频区域时，例如在表面肌电图 (sEMG) 谱在肌肉疲劳期间，从未对估计结果进行过全面评估。因此，本研究的目的是比较 Welch 和自回归参数方法在模拟严重肌肉疲劳的合成 sEMG 信号上的性能。此外，还分析了这两种方法对观测时间和噪声水平的敏感性。结果表明，均值频率很大程度上取决于噪声水平，对于信噪比 (SNR) 小于 10dB 的情况，误差会使估计值不可接受。另一方面，计算中位数频率的误差始终在 2-10Hz 范围内，因此在跟踪肌肉疲劳时应优先选择该参数。结果表明，自回归模型始终优于 Welch 技术，三阶连续产生准确和精确的估计；因此，在分析严重疲劳收缩时，应使用后者。
