# Classification and Regression of Muscle Neural Signals on Human Lower Extremities via BP_AdaBoost
## 2 Materials and Methods
### EMG Signal Acquisition and Noise Reduction  
We selected 10 healthy students (5 males and 5 females) in this experiment, which had no motor dysfunction or congenital diseases. Before the test, let experimenters have enough rest for 48 h, remove their hair on the test muscle surface, wipe the muscle surface with an alcohol cotton ball, stick the positive and negative electrode patches of the EMG sensor firmly along the long axis of the biceps femoris and rectus femoris, and place the reference electrode in the tensor fascia lata bundle. The EMG signal acquisition equipment is IWorxBIO8, which is 8 channel EMG signal collector. The upper computer adopts LabScribe integrated software, which can be output directly in.csv format file for classification algorithm recognition. According to the motion pattern of human walking, a timer is used to ensure that 5 actions are completed within 2 s to ensure the synchronization of data sampling. Each movement was collected for 10 cycles independently. In order to let the experimenter's muscles rest fully, the experimenter rested for 10 min between the test groups each time.  
Primitive EMG signal is a weak neural signal, which can characterize the movement of limbs to a certain extent [17]. In the process of limb movement, it is a non-stationary signal in shape and doped with a variety of noise (white noise, baseline offset, and power frequency interference) [17]. According to the characteristics of EMG signal and noise, Butterworth band-pass filter is used in this article to filter signals other than 20-500 Hz, and the passband cut-off frequency is set to 500 Hz and the stopband cut-off frequency is set to 600 Hz; Butterworth notch filter is used to filter 50 Hz frequency to eliminate power frequency interference, and the signal is passed through high pass filter (cut-off frequency 51 Hz) and low-pass filter (cut-off frequency 49 Hz), respectively. Then, the elliptical filter is used to improve the baseline drift of the signal. The window length of the elliptical filter is 100 ms, the attenuation coefficient beta set as 100, and the passband ripple devel set as 0.005. The filtering results are shown in Figure 2 below. The black curve above is the original EMG signal collected by experiment, and the red curve is the filtered signal of the following. It clarifies that the filtering algorithm retains the characteristics of the signal and removes most noise.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{2}{c}{**Biceps F.**} & \multicolumn{2}{c}{**Rectus F.**} & \multicolumn{2}{c}{**Vastus I.**} & \multicolumn{2}{c}{**Vastus L.**} & \multicolumn{2}{c}{**Vastus M.**} \\ \cline{2-10}  & **R** & \(\mathbf{p}\) & **R** & \(\mathbf{p}\) & **R** & \(\mathbf{p}\) & **R** & \(\mathbf{p}\) & **R** & \(\mathbf{p}\) \\ \hline Biceps F. & 1 & \(-\)0.932 & 0.000 & \(-\)0.468 & 0.001 & \(-\)0.468 & 0.001 & \(-\)0.467 & 0.001 \\ Rectus F. & & 1 & 0.756 & 0.000 & 0.756 & 0.000 & 0.755 & 0.000 \\ [PERSON] I. & & & & 1 & 1 & 0.000 & 1 & 0.000 \\ [PERSON] L. & & & & & 1 & 1 & 0.000 \\ [PERSON] M. & & & & & & & 1 & \\ \hline \end{tabular}
\end{table}
Table 1: Correlation of thigh muscles.