## 5 Experiment and Results
### Comparison of Result with Recent Feature-Extraction Methods  
In this section, we conducted a comparative analysis of our proposed method against existing sEMG-based gesture recognition techniques to underscore the superiority of our approach, as illustrated in Table 4. Recognizing the substantial variations in data collection conditions and environmental factors, we followed the settings outlined in [21] and selected a set of machine learning and traditional deep learning methods for comparison. To ensure fairness and minimize the influence of data quantity variations due to different acquisition devices, we adopted a uniform approach, using sEMG data from eight channels. Our comparison was conducted using the BioPatRec DB2 dataset, which encompasses data from 17 subjects and includes 26 distinct gestures, making it the dataset with the highest gesture diversity among those considered for comparison, thus presenting greater recognition complexity. There are a total of five methods participating in the comparison, as shown in Table 4. The authors of [18] investigated the effects of different window sizes and overlaps on machine learning performance, and developed a novel multi-window majority voting strategy to improve hand gesture recognition accuracies using electromyography signals. The authors of [38] introduced an innovative hand gesture recognition system that utilizes long short-term memory (LSTM) deep learning algorithms to classify hand gestures by training and testing the collected inertial measurement unit (IMU), electromyographic \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Participants** & **Gesture** & **Channels** & **Accuracy** \\ \hline Machine learning with & & & & \\ multi-window & 40 & 6 & 8 & 80.70\% \\ majority Voting [18] & & & & \\ Deep Learning [38] & 10 & 10 & 8 & 77.78\% \\ Machine learning with & & & & \\ the sequential floating & 21 & 17 & 8 & 84.50\% \\ feature selection & & & & \\ method [39] & 4 & 7 & 8 & 89.20\% \\ Machine Learning & & & & \\ with SPD-manifold & 10 & 11 & 8 & 84.85\% \\ based features [21] & & & & \\ This work & 17 & 26 & 8 & **86.37**\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with recent pattern-recognition research. Bold indicates the optimal result among all methods. Figure 5: The F1-score of all datasets is tested by five classifiers on seven methods. (EMG), and finger and palm pressure data. The authors of [39] studied the feasibility of using electromyography (EMG) signals for hand gesture recognition, with a focus on comparing the effects of recording EMG signals from the forearm versus the wrist. The results showed that wrist EMG signals have higher signal quality metrics for subtle finger motions, while maintaining comparable quality for wrist motions. The authors of ref. [36] presented an EMG acquisition and gesture recognition system based on an embedded platform, which employs customized analog front-end circuits and digital signal processing algorithms to achieve high-precision and real-time gesture recognition. The authors of ref. [21] proposed a myoelectric pattern-recognition method based on the SPD manifold, which utilizes the SPD manifold to extract spatial structural information from electromyography signals as features for hand gesture recognition. Table 4 clearly demonstrates that our proposed method is highly competitive when juxtaposed with recent studies. This work achieved an accuracy rate of 86.37%; while this is slightly below the 89.2% reported in ref. [36], it is essential to note that [36] focused on classifying only 7 gestures, whereas our method addresses 26 gestures and still attains remarkably close results. This underscores the remarkable effectiveness of our approach. Notably, in the context of the machine learning with multi-window majority voting technique, which utilized data from 40 subjects performing six different gestures, our method showcased a performance improvement of 5.67%.