# Classification and Regression of Muscle Neural Signals on Human Lower Extremities via BP_AdaBoost
## 3 Results and Discussion  
### Movement Classification Based on BP_Adaboost  
To make the movement separable, this study selects five movements related to knee movement, but the movement modes are different. The similarity of the five movements is that the movement process involves the flexion and extension of knee joint, and the difference is that the muscle force generation modes are different in the movement process. Simultaneously, the feasibility of using the EMG signal as the recognition input and the performance of classifier can be verified. The details are as follows:The BP neural network is set as a single hidden layer structure, including an input layer (two nodes, i.e., two muscle activity eigenvalues of two muscles), a hidden layer (including five nodes), and an output layer (including five nodes, i.e., five actions). The learning rate is 0.1, the epoch is 50, and the convergence error is 0.00004; 5 actions are categorized into 1, 2, 3, 4, and 5, respectively. The sampling frequency of the EMG signal is 2000 Hz and 2 s is a movement cycle. Therefore, the 10-cycle signal of each action has 40,000 data points and the size of the total data set is 200,000. We take 70% as the training set, 15% as the verification set, and 15% as the testing set in order to compare the classification effects of BP and BP_AdaBoost. Figure 7 shows the training results by the BP neural network. The best validation performance is 0.1512 at epoch 45. The number of iterations is 45. Figure 8 shows the classification error of five actions. Zero on the vertical axis indicates that the classification is correct. The recognition rates of five actions by the BP neural network classification are 80.2%, 71.9%, 74.9%, 79.3%, and 87.8%, respectively. The average recognition rate is 78.82%.  
Figure 6: Five kinds of action demonstration: (**a**) High-raising; (**b**) Calf-raising; (**c**) Squating; (**d**) Knee Bending on Chair; (**e**) Walking.  
To classify five different knee movements, the data set (muscle nerve activation) was entered into the BP_AdaBoost strong classifier, whose weak classifier number is to 10. The scale assignment of the data set is the same as that of the BP neural network classifier. The results are revealed in Figures 9 and 10 below. They illustrate that BP_AdaBoost greatly improves the BP neural network. The average recognition rate of BP_AdaBoost is 93.52%, which is 14.7% higher than that of BP neural network (78.82%). The figure on the right shows the BP_AdaBoost has a higher lifting rate of five actions than the BP neural network. The calculation formula is:  
\[r=\frac{m_{2}-m_{1}}{m_{1}}\times 100\% \tag{14}\]  
where \(r\) is the improvement of recognition rate by BP_Adaboost, \(m_{2}\) is the recognition rate with BP_Adaboost as the classifier, and \(m_{1}\) is the recognition rate with BP as the classifier. It can be found that BP_AdaBoost can improve the recognition of knee movement from the BP network. The BP_Adaboost recognition mean is 93.52%. Which is only 78.82 in the BP network. The average promotion rate reaches 18.6%. The maximum is knee flexion, and the value reaches 26.0%. The minimum is walking, and the value reaches 11.8%.  
Figure 8: Classification error of BP network.  
Figure 7: Best validation.