## 2 Methods
### Signal Processing  
The method of muscle evaluation is to express the activity of the muscle by changing the potential of the electromyogram. When the EMG signal is analyzed out of the time domain and the frequency  
Figure 1: The vastus medialis obliquus training system architecture. domain, the following three types of message can be obtained: the temporal signal represents the start time and end time of the muscle contraction triggered, and shows the change of muscle contraction at a certain point in time; the force signal represents the proportional relationship between the myoelectric signal and the contractile force produced by the muscle, explaining the state of muscle activity by the amplitude; the fatigue signal represents the fatigue condition of exhausted muscle, in which the high frequency part of the EMG signal is reduced, and the relatively low frequency part is increased, which also represents a significant decrease in the overall frequency [23; 24]. The EMG characteristics and parameters for assessing muscle function were mainly in the EMG time domain. The value obtained by the integral is proportional to the strength of the muscle contraction force. This was used to calculate the muscle contraction activity per unit time and expressed by integral electromyography (iEMG), as the root mean square value (Root Mean Square, RMS) was calculated as well. Once the fatigue is happened, the high frequency of EMG decreased but the low frequency of EMG increased. It showed that the signal of electromyography is calculated in time domain of EMG. Then the integrated electromyography (iEMG) and the root mean square (RMS) are also calculated based on the integral value and the strength of muscle contraction. Equations (1) and (2) indicates the amplitude index of myoelectric signals; it sums the signals in the time period to evaluate the strength of the EMG signal [25]. The larger the sum value, the larger the myoelectric signal during the cycle time. \[iEMG=\int_{t}^{t+T}EMG(t)dt \tag{1}\]  
\[EMG_{rms}=\sqrt{\frac{1}{T}\int_{t}^{t+T}EMG^{2}(t)dt} \tag{2}\]  
The frequency domain analysis is based on the frequency variation characteristics of the EMG signal. It performs Short-Time Fourier Transform (STFT) on the EMG signal to obtain the Power Spectral Density (PSD) of the EMG signal and shows the myoelectric signal at those frequencies and at their distribution ratio. Frequency domain analysis is usually based on Mean Frequency (MNF) and Median Frequency (MDF), as shown in Equations (3) and (4) [26]. They were used to judge the fatigue of the muscles and show a linear decline in the dynamic process. This feature can be used as a reference for the development of muscle fatigue. \[MNF=\frac{\int_{0}^{\infty}fP(f)df}{\int_{0}^{\infty}P(f)df} \tag{3}\]  
\[MDF=\frac{1}{2}\int_{0}^{\infty}P(f)df \tag{4}\]  
Muscle fatigue occurs due to long-term muscle contraction. In the frequency domain, MNF or MDF frequencies that show the occurrence of muscle fatigue caused by lower frequency shift should be reminded to avoid injury [27]. The signal processing is based on the references. Compared with the walking state, the VMO increase and the VL decrease occur in the running state indicated that the VMO would be weak in the walking condition. Currently, there is no evidence of whether the distribution of these muscle forces will cause pain or result in illness but increasing co-contraction can improve knee joint stability and helps to adjust the patella's gliding trajectory. The Contraction Threshold (CT) was defined as the level of static level signal plus 3 times Standard Deviation (\(\sigma\)), as shown in Equation (5). The CT value was used to extract the segment caused by the action in the myoelectric signal, and the static level signal portion was excluded to reduce the total number of signal calculations and also to highlight the myoelectric signal caused by the action.