## 5 Experiment and Results  
### Comparison of the Original Data and Data after Extending Function  
In this section, we compared the performance of different feature-extraction method combinations and analyzed the classification accuracy when employing the space-to-space (SPS) technique. Our test involved classifiers such as LDA, SVM, KNN, and ANN, conducted on the BioPatRec DB1 dataset, and the results are presented in Table 2 and the accompanying graph. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
\begin{tabular}{c} 2000 Hz \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of the sEMG databases used in this paper. From the table, it is evident that the utilization of SPS for expanding the original sEMG signal segments, in conjunction with various feature-extraction methods, has led to a significant improvement in overall accuracy. The average classification accuracy of the four classifiers increased by 2.39%, 1.5%, 1.7%, and 2.49% after using the SPS technique. This underscores the effectiveness of SPS, as it allows for the extraction of more feature information within individual channels, thereby enhancing the accuracy of gesture classification. Furthermore, It is worth noting that when utilizing a single-feature-extraction method, the use of SPS results in a more pronounced enhancement in classification performance. This phenomenon may be attributed to the inability of a single-feature-extraction method to fully exploit the latent features within sEMG signals. The incorporation of SPS allows for the extraction of additional features, enriching the feature set and ultimately improving recognition accuracy. Additionally, Figure 4 illustrates the normalized confusion matrix obtained using our proposed approach with an KNN classifier for the BioPatRec DB1 database, with gestures represented by numbers 0 through 9. A majority of the original labels were correctly predicted, and the proposed approach achieved an average classification accuracy of 88.4% across all 10 gestures, with the best class achieving 95.52% accuracy. This further confirms the robust performance of our proposed approach. Figure 3 illustrates the different classification results with and without the involvement of SPS. It is evident that the inclusion of SPS leads to an enhancement in the classification performance of the classifier. This improvement is particularly notable in categories where the previous recognition accuracy was lower, consequently resulting in an overall elevation of the accuracy in EMG pattern recognition. \begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **LDA** & **SVM** & **KNN** & **ANN** \\ \hline TD & 50.88 & 30.17 & 64.43 & 85.09 \\ TD \(\dagger\) & **52.15** & **36.59** & **65.65** & **85.95** \\ \hline mDWT & 47.29 & 63.90 & 74.57 & 79.53 \\ mDWT \(\dagger\) & **48.55** & **65.19** & **77.05** & **81.52** \\ \hline ICC & 39.04 & **39.39** & 69.59 & 76.56 \\ ICC \(\dagger\) & **41.76** & 38.94 & **70.51** & **78.08** \\ \hline TD and mDWT & 53.27 & 61.85 & 78.71 & 85.19 \\ TD and mDWT \(\dagger\) & **55.60** & **63.90** & **81.41** & **87.25** \\ \hline TD and ICC & 52.39 & 43.60 & 79.07 & 85.91 \\ TD and ICC \(\dagger\) & **55.59** & **44.86** & **79.27** & **86.84** \\ \hline ICC and mDWT & 50.48 & **60.27** & 74.66 & 84.01 \\ ICC and mDWT \(\dagger\) & **53.43** & 59.28 & **77.24** & **85.09** \\ \hline ICC and TD and mDWT & 54.60 & 59.69 & 78.71 & 86.36 \\ ICC and TD and mDWT \(\dagger\) & **57.81** & **61.00** & **81.57** & **88.40** \\ \hline \end{tabular}
\end{table} 
Table 2: Ablation experiments of SPS technique, where \(\dagger\) represents the operation of expanding sEMG signals, testing on BioPatRec DB1 database. To clearly describe the final data input format for each feature domain, let us consider signal segments \(X(c\times n)\) and \(X^{\prime}((c\times scale)\times\frac{n}{scale})\) as examples.