## 1 Introduction  
The electromyogram (EMG) signal is a vital biosignal produced by the electrical activity of muscle fibers during contraction or relaxation, and can be detected on the surface of the skin [1]. The EMG signal is a non-stationary random signal and usually requires noise reduction and signal amplification [2]. The development of EMG signal recognition systems has become a research hotspot [3; 4; 5; 6; 7]. However, in these studies, there are few applications related to forestry machinery. Based on the wireless remote-control automatic wood pruning machine developed in Japan [8], we have developed the BSR-Z23 radio remote automatic pruning machine [9], which can effectively improve the pruning efficiency and ensure the personal safety of the operators. At present, the automatic pruning machine control method is the traditional button type control [10]. Compared with the push-button control, the gesture control method based on the sEMG signal makes the consciousness of the human body and the mechanical device uniform. With the characteristics of straightforwardness and flexibility, it can realize natural control, intuitive operation [11]. The advantages have practical significance for the operations in the forest area, and it is easy to develop portable equipment, which provides a new idea for promoting the creative and human computer interaction process of forestry machinery [12]. The success of the EMG control scheme depends largely on feature computation and selection. So far, no studies have identified what types of features can fully express the mathematical propertiesof sEMG signals. If too many features are selected, the calculation and complexity of the classifier will be increased, and the recognition efficiency will be reduced. If too few features are selected, the information contained in the EMG signal will be lost and not contribute to the classification in subsequent identification steps. At present, the sEMG signal feature calculation methods mainly include time-domain, frequency-domain, time-frequency-domain, and nonlinear dynamics analysis [12; 13; 14]. In time-domain analysis, there are mean absolute value [15], root mean square [16], Wilson amplitude [15], zero-crossing rate [17], wavelength [18], and some time-domain model parameters, such as auto-regressive parameter model coefficients [17]. Power spectrum analysis is commonly used in frequency-domain analysis methods. Time-frequency domain analysis includes wavelet decomposition [19] and wavelet packet decomposition. Among the nonlinear dynamics methods, calculation of the information entropy [20; 21] and the maximum Lyapunov exponent [22] are the main methods. To reduce the dimensionality of signal features, [PERSON] et al. [23] have proposed thirty-seven time-domain and frequency-domain features for studying the characteristics of EMG signals, among which the most recommended ones are the mean absolute value, waveform length, Wilson amplitude, auto-regressive, mean absolute value slope, and mean frequency. [PERSON] et al. [24] have employed a Kernel Least Square Tracker (KRLS-T) to estimate dimensional wrist kinematics from the sEMG signals of forearm muscle groups. But the accuracy is not high enough. In conjunction with wavelet analysis, [PERSON] et al. [25] have used principal component analysis (PCA) and feature selection algorithms to reduce the number of dimensions and achieve automatic detection of EMG signals. [PERSON] et al. [26] combined LDA with the wavelet packet transform to develop a novel feature selection method based on a depth recursive search algorithm. Such that the high-dimensional features can be reduced using a supervised feature reduction algorithm. [PERSON] et al. [27] have proposed a multi-scale bidirectional two-dimensional PCA method for high-dimensional biomedical signal analysis, which realized the dimensionality reduction of high-dimensional signals and proved to be highly efficient. [PERSON] et al. [28] have applied LDA classify the sEMG signals from 2 electrodes and recognize the grasping force.