## 6 Discussion  
Muscle fatigue induced by long walking time changes the characteristics of sEMG signals. In this way, muscle fatigue affects the performances of the gait phase classifiers, potentially harming lower-limb wearable robots' ability to provide effective assistance. The practical applications of the gait phase classifiers in wearable robots demand a deeper understanding of the relationship between muscle fatigue and the classifiers' performance. Our study mainly performs the following three aspects of work: (1) A quantitative investigation on the impact of muscle fatigue on the performance of the classifiers using different \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Statistic** & **Factor** & **MAV** & **ZC** & **WL** & **SSC** & **RMS** & **MDF** & **MPF** & **SMA** \\ \hline \multirow{2}{*}{Mean} & Fatigue & 20.79 & 27.70 & 17.95 & 17.59 & 20.05 & 20.65 & 26.84 & 1.57 \\  & Individuals & 3772 & 3724 & 1810 & 4849 & 3463 & 4039 & 4008 & 0.02 \\ \hline \multirow{2}{*}{Variance} & Fatigue & 10.67 & 9.26 & 20.17 & 10.08 & 10.90 & 20.01 & 27.02 & 28.87 \\  & Individuals & 391.3 & 349.9 & 398.3 & 534.9 & 382.9 & 610.1 & 450.1 & 1038.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The F values for the MANOV of the features. Figure 8: The mean values are calculated from the EMG features. The figures are plotted separately for each of the features: (a) MAV, (b) ZC, (c) WL, (d) SSC, (e) RMS, (f) MDF, (g) MPF, (h) SMA. features; (2) a training strategy that improves the robustness of the classifiers against the fatigue; (3) a statistical analysis to interpret the phenomena of the classification. These works can provide a reference for other studies, such as designing the robust human-machine interface and investigating muscle fatigue-induced sEMG changes. Our results indicate that muscle fatigue has significant effects on the classifiers that use the features, including MAV, RMS, ZC, SSC, MDF, and MPF. Moreover, for the different features, the effects are not the same. For the single feature, SMA and WL may be the best choices if considering the robustness against fatigue. ZC and SSC present larger errors in our study both on the generalization to muscle fatigue and the accuracy of the classification. One of the possible reasons for still including them in the feature set is that they can improve the performance of the model when combined with other features. The effects of muscle fatigue on the accuracies of the classifiers seem to be different from the phenomenon reported by the study [21]. They have performed a similar study that also used the MAV, ZC, WL, and SSC to train the classifiers by nonfatigue data, while testing them by fatigue data. Their results presented that muscle fatigue did not significantly affect the classifiers' accuracies. The reason for this divergence may be the different ways of inducing fatigue. In their study, they used the 90-s isometric contractions to induce short-term fatigue, while in our study, we investigate the effects of long-term fatigue. We choose long-term fatigue to conduct our study, since long-term fatigue seems to be more common fatigue during the usage of the lower-limb wearable robots. By the practical test, we validate our proposed training strategy. This strategy is adding some of the fatigue data into the training set, which is pretty simple, but really works. As mentioned above, we add the first half of the fatigue data into the training set and use the second half data as the test set. The performance improvement may indicate that the subjects reach a stable state of fatigue after long-term walking. In this way, the data distribution might not change in the second half data of the third session. Therefore, the fatigue data adding into the training set can improve the performance of the classifiers under fatigue. Through the analysis, we preliminarily explain the phenomena of classifier degradation in this study.