## 5 Discussion  
The results indicate that classifiers that use multiple features outperform those with a single feature, with an average accuracy of 85%. This is consistent with the current state-of-the-art SVM classifiers using acquisition devices at 200 Hz, such as those by [PERSON] et al. [28, 29]. However, there are two key differences in the feature selection of this project.  
First, no frequency-domain features, such as mean frequency or cepstral coefficients, were used in the training process. This significantly reduces the computational time since the Fourier Transform is not required. The second difference is that two well-known time-domain features, Zero Crossing (ZC) and slope sign change (SSC), were also excluded. While these features are common in sEMG analysis, they require the definition of a threshold, which introduces an additional parameter to tune during SVM training. Discarding these features is a step toward generalizing and simplifying the classifier.  
To better understand the accuracy obtained, it is necessary to delve into the _anatomy_ of the classifier. As previously described, the multiclass SVM consists of 36 individual binary SVMs, one for each pair of possible postures. This means that the overall accuracy of the model is influenced by the performance of each individual SVM. Examining their individual accuracy reveals that some postures are highly separable from others. For instance, the SVMs responsible for recognizing the \"pointing index\" posture achieve accuracy between 98% and 100%. This posture is highly distinguishable from the other postures using this feature set.  
Conversely, the SVM responsible for classifying the \"cylindrical grasp\" and \"hook grasp\" shows significantly lower accuracy, around 79% to 82%. This indicates that, with this feature set, these two movements are not highly separable, which lowers the overall accuracy of the classifier. This behavior is evident in the confusion matrix (Figure 5), where misclassification between these postures is more common.  
As mentioned earlier, the 'pointing index' posture is highly separable from the other postures, as every predicted class matches the true class. However, the remaining postures are not perfectly separable, and the predicted class does not always correspond to the actual class.  
The present work also contributes to the existing literature. Other SVM approaches for upper limb movement classification do not address grasping postures in activities of daily living or focus on only a subset of them. In this project, the nine most common grasping postures used in daily life activities are considered, with the goal of restoring as much dexterity as possible to prostheses users.  
Finally, regarding the real-time low-cost implementation, improvements have been made with respect to the authors' previous work presented in [19]. In that work, an NN-based algorithm was developed that took 600.66 ms to recognize a grasping posture from the sEMG signal provided by the Myo armband, with a global percentage success of 73%. In the present work, a total time of 265 ms is needed for the whole classification process with an accuracy of 85%. These results imply a reduction of the computational time of 56% and an increase in accuracy of more than 16%. In addition, the time required in MATLAB is 265 ms and it should be shorter on a low-cost specific microprocessor. As the classification time required is small, the grasping posture recognition of nine hand postures using SVMs seems feasible for low-cost platforms.