## 4 Results  
The first set of experiments aimed to evaluate the classifier. Initially, individual features were tested for accuracy across the 10 subjects (Table 4). A simple glance at the table reveals a noticeable difference in accuracy when comparing the time-domain features to the frequency-domain features (MPF and MF). This performance gap is largely due to the sampling frequency of the acquisition device. The Myo armband's electrodes capture sEMG signals at 200 Hz, which effectively covers the dominant energy range of surface sEMG signals, typically between 50 Hz and 150 Hz. However, certain frequency components can extend up to 500 Hz, a bandwidth beyond the Myo armband's capabilities [27]. The exclusion of these higher frequencies results in poorer classification accuracy. Consequently, frequency-domain features were discarded for sEMG signal classification. Table 5 presents the classification accuracy using three different feature sets: Group 1 (WL, MAV, and SD), Group 2 (IEMG, MAV, and RMS), and Group 3 (IEMG, WL, MAV, and VAR). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Subject** & **MAV** & **SD** & **IEMG** & **WL** & **VAR** & **RMS** & **MPF** & **MF** \\ \hline
1 & 78\% & 78\% & 79\% & 76\% & 75\% & 80\% & 40\% & 31\% \\
2 & 81\% & 80\% & 81\% & 80\% & 79\% & 83\% & 42\% & 36\% \\
3 & 86\% & 85\% & 85\% & 84\% & 82\% & 84\% & 32\% & 27\% \\
4 & 70\% & 71\% & 70\% & 69\% & 70\% & 72\% & 31\% & 26\% \\
5 & 76\% & 76\% & 77\% & 75\% & 73\% & 78\% & 44\% & 35\% \\
6 & 80\% & 79\% & 81\% & 75\% & 74\% & 77\% & 38\% & 34\% \\
7 & 78\% & 75\% & 75\% & 72\% & 73\% & 76\% & 39\% & 33\% \\
8 & 80\% & 78\% & 79\% & 79\% & 76\% & 78\% & 47\% & 40\% \\
9 & 85\% & 83\% & 85\% & 80\% & 84\% & 86\% & 52\% & 43\% \\
10 & 80\% & 77\% & 79\% & 79\% & 75\% & 79\% & 42\% & 36\% \\ Mean & 79\% & 78\% & 79\% & 76\% & 76\% & 79\% & 40\% & 34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy for every subject of the single feature classifier for 9 grasping postures. Figure 4: Example of multi-class classification using multiple SVMs. The data to be labeled pass through every classifier, and the result that appears the most is the final label assigned to the data [23]. As can be observed, a multi-feature classifier performs better than those trained with a single feature, achieving an average accuracy of 85% with the Group 2 features while requiring slightly less training and computational time. The confusion matrix (Figure 5) shows the predicted versus actual labels for each grasping posture classification. In this figure, each number represents a grasping posture: (1) pointing index, (2) cylindrical grasp, (3) hook grasp, (4) parallel grasp, (5) oblique palmar, (6) intermediate power precision, (7) pinch grasp, (8) lateral pinch, and (9) special pinch. Regarding the implementation of the algorithm, it takes an average of 15 ms to extract the features and classify the signal in MATLAB, running on a laptop with an Intel Core i5-8300H 2.3 GHz CPU and 8 GB of RAM. In order to check the feasibility of the real-time implementation of this classifier on a low-cost platform, time calculations have been performed. The algorithm devotes 15 ms to feature computation and classification. Adding the 250 ms required for signal acquisition, the overall process takes an average of 265 ms, which complies with the 300 ms limit reported in the literature. There are some differences in pattern recognition depending on the grasping type. Hook grasp (number 3 in Figure 5) is sometimes confused with cylindrical grasp (number  
Figure 5: The confusion matrix of the classifier trained with features MAV, IEMG, and RMS (Group 2) and each number representing a grasping posture.