## 1 Introduction  
Lower-limb wearable robots, a kind of powered device that can enhance the mobility of the human body, have been widely used for assistive and rehabilitative purposes in recent years [1, 2, 3]. Regarding the control of wearable robots, surface Electromyography (sEMG) provides an intuitive signal source that can directly reflect users' motion intent, and thus, can assist in involving the active participation of users [4, 5, 6, 7]. Among the applications of sEMG signals in lower-limb wearable robots, gait phase classification provides the fundamental information of lower-limb movements, thus can form effective assistance and can be used in disease diagnosis [8, 9]. Much research that classified gait phases used multiple features and classifiers with accuracy over 90%, which are of practical value [10, 11, 12]. However, as indicated in the study [13], the nonstationary characteristics of sEMG signals that can be attributed to many extrinsic or intrinsic factors might limit the broader applications of sEMG. Among all the factors that might affect the performance of the sEMG-based classifiers, muscle fatigue is the intrinsic factor that seems to be an unavoidable factor, especially during the long-term usage of wearable robots. Fatigue is defined as the decrease in muscle contractility after prolonged excises, usually measured through the muscle force deterioration during the maximum voluntary contractions. Many studies have reported some basic findings in the domain of sEMG and fatigue. One conclusion is that muscle fatigue will induce the dynamic range increase and the frequency decrease of the sEMG signals [13, 14, 15]. Based on this phenomenon, a lot of features, such as root mean square (RMS) [16, 17], median frequency (MDF) [17, 18], mean power frequency (MPF) [18; 19], and wavelet transform (WT) [19; 20], have been studied to indicate the development of muscle fatigue, and research suggests their availabilities as the indices of fatigue. These studies usually plotted the values of the employed features in the course of time, without considering how the changes of the values affect the performance of classifiers. Regarding the fatigue-induced change of the sEMG-based classifiers' performance, several studies have been performed. [PERSON] et al. [21] studied the influence of three factors, including muscle fatigue, on the classification of four contraction patterns, which focused on time-domain features. Bukhari et al. [22] performed the experiments to investigate the performance of the K-nearest neighbor (KNN) classifier, which was used to classify contraction patterns of biceps brachii under muscle fatigue. Although presenting impressive inspirations, how the non-fatigue data-trained classifiers would deteriorate on fatigue data might still be a research gap. Moreover, as for the gait phase classification, how muscle fatigue affects the performance of the gait phase classification is still unclear. Pioneer studies also focused on improving the classifiers' robustness against muscle fatigue. [PERSON] et al. [23] and [PERSON] et al. [24] have proposed EMG based gesture classifier, which is robust to fatigue by using a fuzzy neural network and an adaptive incremental hybrid classifier-based recognize strategy, respectively. [PERSON] et al. [25] developed a new double differential electrode that can compensate for the influence of muscle fatigue by modifying its gain. Those efforts are directed to measuring the impact of fatigue on locomotion mode classification and attempt to build anti-fatigue classifiers. However, these studies either require a relatively large computation, due to the employed complex methods or require specially designed equipment. These might deviate from the easy-to-use demand, thus might limit the broader applications. In our study, we propose a training strategy to improve the classifiers' robustness against muscle fatigue, which could be a simpler way.