## 4 Conclusions  
In this paper, we first classify six gestures from 15 healthy subjects using self-acquired signals (252-dimensional feature vectors), compare feature selection methods and classifiers with better classification effects, and compare different. Whether there are individual differences between the examiners is explored. The results indicate that there are no individual differences between subjects. Next, using the benchmark dataset (630-dimensional feature vector), the PCA and LDA dimension reduction methods are combined with the DA classifier to explore the effect of different numbers and different gestures on the optimal dimension. The experimental results show that the maximum accuracy of the four types of gestures is up to 100.0% after PCA dimensional reduction, and the optimal size is \(511\pm 12\). The highest accuracy after LDA reduction is only 73.94%, and the optimal size is \(35\pm 24\). The performance of PCA is better than LDA, both in terms of classification accuracy and stability of optimal size. For the six types of gestures, the highest classification accuracy after PCA reduction is 98.29%, and the optimal size is \(542\pm 26\). This shows that as the gesture movement increases or decreases, the optimal size of the PCA dimension reduction is not large, basically between 516 and 523 dimensions. The difference in the dimensionality of the two data sets may be due to the difference in the number of electrodes used in the two data sets during signal acquisition. The first data set  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gesture Combinations** & **Best Classification Rate (\%)** & **Optimal Dimensions** \\ \hline  & 98.29 & 567 \\ \hline  & 98.21 & 549 \\ \hline  & 97.88 & 533 \\ \hline  & 97.71 & 508 \\ \hline  & 97.71 & 560 \\ \hline  & 97.70 & 535 \\ \hline  & 97.64 & 527 \\ \hline  & 97.16 & 517 \\ \hline  & 97.15 & 551 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracy of six gesture combinations after PCA feature selection.  
was acquired using four electrode patches, while the second data set was acquired using ten patch electrodes. The multiplication of the number of electrodes will undoubtedly increase the complexity of the signal. If a high-precision classification is desired, the dimensionality reduction will not be large. This also inspired us that the dimension of the sEMG signal is not as high as possible. We need to choose different dimensions according to the different requirements of the application. In general, the research results can meet the accuracy requirements of the automatic standing wood pruning machine for the classification of myoelectric signals. It provides a platform and extension for new control methods for pruning machines. The experimental results can be applied not only to the control method of the pruning machine but also to the forestry machinery below the six control commands, demonstrating the application of sEMG-based gesture recognition technology in the field of forestry machinery intelligence and human computer interaction.  
Conceptualization, W.L. and J.K.; methodology, J.K.; data curation, Y.W., X.H., and Z.W.; formal analysis, Y.W., X.H., and J.W.; supervision, J.W. and W.L.; writing--original draft preparation, Y.W.; project administration, J.W.  
This research is financially supported by the Ningxia Hui Autonomous Region science and technology key R&D project (No. 2018 BBF02020).  
The authors declare no conflicts of interest.