## 2 Materials and Methods  
### Materials  
We applied the RM6240B multi-channel physiological signal acquisition and processing system to acquire the sEMG signal. The signal sampling frequency reached 100 up to kHz, and it simultaneously acquired four channels of signals. The sampling frequency of the acquisition device was set to 1000 Hz. According to the operation command of the forestry machinery, combined with the function of the forearm muscle, we designed six types of gestures [29], which comprised fist, palm lateral supination, palm lateral pronation, palm supination, palm pronation, and finger spread, corresponding to median,ascending, descending, idle, operation, and walking, respectively. We used patch electrodes to collect 4-way sEMG signals derived from the palmaris longus muscle of the forearm, the brachioradialis muscle, the extensor digitorum muscle, and the extensor carpi ulnaris muscle. The placement of the chip electrodes is shown in Figure 1. The determination of the target muscle was based on the following factors: first, muscles in the shallow forearm were preferentially selected; second, the function of the muscles corresponds to the gesture movement; finally, the position of the selected muscles is easy to find. The average age of the subjects was 23 years, including 12 males and three females. The subjects had no neuromuscular disease. In the last six months, there were no injuries to the forearm that affected the movement of the hand.  
In the signal acquisition, each group experiment included six actions, which were fist punching, punching, valgus, inversion, upper cutting, and lower cutting. The following action sequence was used to collect each set of motion signals: gesture 1 for 2 s \(\rightarrow\) relax for 5 s \(\rightarrow\) gesture 2 for 2 s \(\rightarrow\)... \(\rightarrow\) gesture 6 for 2 s. Once an action sequence was completed, the subject had a 2-min relaxation period to prevent muscle fatigue. The acquisition instrument recorded the EMG signal continually, so the segment of sEMG signals contained both the motion signal segment and the no-action signal segment. Finally, 150 4-way motion signals were obtained in the acquisition experiment, in which each segment contained six myoelectric features of gestures. The signals obtained after pre-processing constituted the data sets of this study. The data set was tagged for the calculation of classification accuracy.  
The signal pre-processing process included mainly noise cancellation, amplification, and active segment detection. First, 50 Hz power-frequency interference and other noise in the sEMG signal were eliminated by using a 50 Hz notch filter and a bandpass filter that allowed only frequency components between 10 and 450 Hz to pass. Then, the amplitude of the sEMG signal was amplified by a factor of 1000. Finally, to confirm the starting point and end of the motion, the moving average method was used to detect the active segment of the sEMG signal. The result of the active segment detection is shown in Figure 2. The red \"\(\times\)\" indicates the starting point of the action, and the green \"\(\times\)\" indicates the ending point of the action. After successfully segmenting the motion signal segment, 1024 sample points were taken in each segment of the signal for calculating features.  
Figure 1: Photo of placement positions of surface electrodes. (**a**) is the abdominal positions of the palmaris longus muscle and brachioradialis muscle; (**b**) is the abdominal positions of the extensor digitorum muscle and extensor carpi ulnaris muscle.  
Figure 2: sEMG signal waveform and activity segment detection.