## 2 Related Work  
Before the statement of our method, it is necessary to introduce some previous research, which provides some inspirations. In order to perform our study, we have to design an appropriate experiment to induce muscle fatigue. The experiment protocols to induce lower-limb muscle fatigue are mainly divided into the following three kinds: Isokinetic contraction, repetitive sit-to-stand, and endurance task. The isokinetic contraction or the repetitive sit-to-stand may not be an appropriate way to induce muscle fatigue-related change in gait. [PERSON] et al. [26] induced the local muscle fatigue by the isokinetic knee extension, and they studied the impact of the fatigue on human gait. Their results showed that the local muscle fatigue induced by the isokinetic contraction only evoked a subtle change in human gait even if the decrease of the maximum voluntary contraction (MVC) reached 50%. [PERSON] et al. [27] reported similar results. In their study, the subjects were asked to perform the sit-to-stand until they were exhausted. The results also showed that the MVC of the subjects decreased by 12% on average, but the gait parameters only changed diminutively. [PERSON] et al. [28] studied the fatigue induced by endurance tasks on a treadmill, and they tested the gait by walking overground. According to their results, obvious gait changes were detected. The results of the above studies indicate that the way to induce muscle fatigue may count, and it is better to select an endurance task similar to the test motion. The conclusion seems to be the common practice for the sEMG-related muscle fatigue studies, such as the studies that induced fatigue by running on a treadmill [29; 30]/overground [31], walking [32], and cycling [33; 34; 35]. In this paper, we study the effect of muscle fatigue in walking, so long walking time on a treadmill was selected as the method to induce fatigue. In our study, muscle fatigue will be investigated through the observation of the classification accuracy, so it is also necessary to appropriately select the features used in the classification. Our intent is not only to cover the features commonly used in the classification, but also to involve the features that can be used in fatigue detection. Many features have been used as indices to measure muscle fatigue in previous research. With the deepening of muscle fatigue, the sEMG signal varies according to a trend of dynamic range increase and frequency decrease [13; 14; 15; 36]. Based on this phenomenon, many studies have reported the abilities of the traditional EMG features to detect muscle fatigue. Root mean square (RMS) was used to detect the amplitude changes [16; 37], while median frequency (MDF) [18; 37] and mean power frequency (MPF) [18; 38] were used to detect the frequency changes. In this paper, these three features are selected. Many studies have reported the effect of muscle fatigue on EMG signals, and some of them also analyze it from the aspect of classification accuracy. Although these works mainly focused on upper limbs, it is necessary to introduce their studies as references to our work. [PERSON] et al. [21] studied the stabilities of eleven features under muscle fatigue by testing the classifiers' accuracies. In their study, the subjects performed arm motions by a customized handle. The short-term muscle fatigue was induced by isometric contractions. The classifiers were used on types of contraction during arm motions, and they trained the classifiers by the non-fatigue data, while testing them by the fatigue data. Their results showed that muscle fatigue significantly affects only one of the eleven features from the perspective of the accuracy of the classifier. However, as mentioned above, using an isometric contraction to induce short-term muscle fatigue may not induce a similar muscle pattern change of long-term walking, thus might not affect the performance of classifiers in the same manner. [PERSON] et al.