# Classification and Regression of Muscle Neural Signals on Human Lower Extremities via BP_AdaBoost
## 3 Results and Discussion
### Knee Angle Regression Based on BP_Adaboost  
It illustrates from the above research that the BP_AdaBoost strong classifier has a good performance in the recognition of typical movements of human lower limbs with EMG signals as input, and BP_AdaBoost can also be used to design the strong regressor. The design of the BP_AdaBoost strong regressor is similar to that of the BP_AdaBoost strong classifier. The difference is that the BP_AdaBoost strong regressor uses the BP neural network as a weak regressor, which is still a single hidden layer structure, including an input layer (two nodes, i.e., two muscle neural activation eigenvalues of two muscles), a hidden layer (including five nodes), and one output layer (the output is the knee angle). The learning rate is 0.01, the epoch is 50, and the convergence error is 0.00004. After training, predict the output of test samples and adjust the weight value of the test samples according to the prediction results. In this article, the threshold is still set at 0.1. Take the test samples that exceed the threshold as the samples that should strengthen learning. The BP_AdaBoost strong regressor is used to predict the knee angle, and the IMU pose sensor is used to measure the angle changes of thigh and calf in the sagittal plane during walking. The measured value is the difference between the limb and the longitudinal axis. Set the thigh angle as \(\alpha\); the thigh angle is positive when lifting the thigh forward and negative  
Figure 10: Improvement effect of BP_AdaBoost.  
Figure 9: Comparison of BP and BP_AdaBoost algorithms.  
when lifting the thigh backward. Set the calf angle as \(\beta\); calf lifting forward is positive and backward is negative. When standing \(\alpha=\beta=0\). The knee angle is defined as the angle between the thigh and the lower leg \(\theta\); the knee angle during walking is calculated as:  
\[\theta=180-\alpha+\beta \tag{15}\]  
Of which: \(\alpha\), \(\beta\), and \(\theta\) are the angles of thigh, calf, and knee, respectively, which is a generation value, \(\theta\). The angle range is 0-180%.  
Firstly, the EMG signals of 10 gait cycles are selected as the data set. The size of the feature window is 200 ms. In order to ensure that the signal is not lost, the size of the sliding window is 100 ms and the size of the overlapping window is 100 ms. The number of weak classifiers is set to 10. Figure 11 shows the knee angle measured by IMU and the knee angle predicted by BP_AdaBoost. The black curve is the measured value by IMU and the red curve is the regression value with the EMG neural signal as the input. It can be analyzed that BP_AdaBoost can well predict the knee angle value, but the output result of the algorithm has certain noise. Figure 12 shows the smoothing of the regression value, and the red curve is the regression value after smoothing. The black curve is the regression value with myoelectric signal as the input (that is the red curve in Figure 11). Comparing Figures 11 and 12, it can be determined that the BP_AdaBoost algorithm can realize the regression mapping of the knee joint angle with the EMG signals of rectus femoris and biceps femoris of the thigh as the input.  
To verify the superiority of BP_AdaBoost strong predictor with other regression algorithms, this article compares the regression effects of decision Tree, SVR, Gaussian  
Figure 11: Measured value and regression value.  
Figure 12: Regression value smoothing.  
classifier, and BP as a weak classifier, selects the knee angle of five actions as target, and compares the regression errors of their algorithms, as shown in Table 2.  
This illustrates that the BP_AdaBoost regressor has the best regression effect. The regression errors of five kinds of knee joint movements were less than 10%. The error of calf-raising is the lowest, only 4.4%, and the average value is 6.384%, which is due to the advantages of the BP neural network algorithm and the characteristics of the EMG signal, as well as the advantages of the BP_AdaBoost combined with the BP weak regressor.