## 3 Experiments  
### Self-Collecting Signals  
After the feature extraction process was performed on the 252-dimensional feature vector, according to experience, the offline classification algorithm was adopted. We chose randomly 80% of the sample as training data, others as testing data. The above six classification algorithms were used to identify the dimension-reduction algorithms of different dimensions. The experimental results are shown in Figure 4. The results of using the k-nearest neighbour (KNN) classifier to test the four feature extraction algorithms are shown in Figure 4a, where the nearest neighbor number k was set to 4. The results of using the random forest (RF) classifier are shown in Figure 4b. The number of trees was set to 50. Because the RF classifier recognition results are different every time, the recognition rate in different dimensions is the average value of three recognition results. The results of using the naive Bayes (NB), discriminant analysis (DA), back-propagation neural network (BPNN), and support vector machine (SVM) are shown in Figure 4c-f, respectively. The training function of the BPNN was set as traincgb, the number of nodes in 13 hidden layers was set, and the training iteration number was 1000. The SVM parameters C and \(\gamma\) were set to 4 and 1, respectively. Table 1 summarizes the results in Figure 4.  
As reported in Table 1, the results of dimensionality reduction using six classifiers show that LDA has the best dimension-reduction effect, and the optimal dimensions of LE and KPCA are generally higher than the optimal dimensions of GP-LVM and LDA. The average of the best dimensions of LE and KPCA is more significant than 100 dimensions, and the average of the best dimensions of GP-LVM and LDA is less than 60 dimensions. When testing with BPNN, the best dimension of GP-LVM appears to be 205 dimensions, which has some contingency. From the six figures, it can be observed that there is a maximum value in low dimensions. To explore the difference in the optimal size number between different subjects, and the effect on different gesture recognition. We used the data which went through LDA reduced dimension as the feature data. Among the specific data of each gesture action of each subject, 80% of them were randomly selected as training data, and the rest were test data, and ten sets of training and test data were randomly obtained. The optimal dimensionality reduction dimension of each classifier was selected. KNN, NB, and DA classifiers used 5-dimensional data, RF classifiers used 6-dimensional data, and BPNN used 8-dimensional data. The final recognition rate was the average of 10 test results. The gesture recognition results are shown in Table 2, and the gesture recognition results for each gesture action are shown in Table 3.  
Figure 4: Test of the effect of feature extraction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Extraction Methods** & **Classification Methods** & **Best Classification Rates (\%)** & **Optimal Dimensions** & **Average of Optimal Dimensions** \\ \hline \multirow{8}{*}{LE} & KNN & 85.56 & 105 & \multirow{8}{*}{104.2} \\  & RF & 88.89 & 159 & \multirow{8}{*}{104.2} \\  & NB & 67.78 & 57 & \\  & DA & 87.78 & 104 & \\  & BPNN & 85.43 & 95 & \\  & SVM & 87.78 & 105 & \\ \hline \multirow{8}{*}{KPCA} & KNN & 85.56 & 249 & \multirow{8}{*}{146.5} \\  & RF & 88.15 & 118 & \\  & NB & 72.96 & 89 & \\  & DA & 84.81 & 137 & \\  & BPNN & 86.79 & 144 & \\  & SVM & 90.74 & 142 & \\ \hline \multirow{8}{*}{GP-LVM} & KNN & 89.26 & 43 & \multirow{8}{*}{57.2} \\  & RF & 83.33 & 39 & \\ \cline{1-1}  & NB & 67.78 & 16 & \\ \cline{1-1}  & DA & 68.52 & 30 & \\ \cline{1-1}  & BPNN & 77.04 & 205 & \\ \cline{1-1}  & SVM & 88.89 & 10 & \\ \hline \multirow{8}{*}{LDA} & KNN & 94.44 & 5 & \multirow{8}{*}{6.0} \\  & RF & 94.44 & 6 & \\ \cline{1-1}  & NB & 97.41 & 5 & \\ \cline{1-1}  & DA & 97.78 & 5 & \\ \cline{1-1}  & BPNN & 97.04 & 8 & \\ \cline{1-1}  & SVM & 94.44 & 7 & \\ \hline \hline \end{tabular}
\end{table}