# Gesture Recognition of Sign Language Alphabet Using a Magnetic Positioning System
## 4 Discussion  
From the experimental results presented in Figures 12-14, it is possible to notice that the best performance is provided by Model B, among the considered models. As shown in Table 5, Model B results in an accuracy of approximately 97%, which is comparable with the best results from the literature presented in Section 1. However, the proposed approach is more robust than the methods from the literature since it uses a tracking system to extract hand features, which makes it less sensitive to environmental conditions, changes in the input device, and signer variability.  
On the other hand, the less complex Model A results in a slightly lower accuracy of approximately 95%. This proves that the kinematic hand model used in Model B for reconstructing the hand position, as explained in Section 2.2, provides richer information with respect to the raw data used by Model A.  
Figure 14: Confusion matrix showing classification performance of the proposed method using Model C, where the SVM input landmarks are given in a coordinate system aligned with the hand.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Model A** & **Model B** & **Model C** \\ \hline Signer 1 & 97.01 & 97.92 & 76.77 \\ Signer 2 & 93.52 & 97.51 & 74.37 \\ Signer 3 & 94.84 & 94.68 & 81.47 \\ \hline
**Global** & 94.95 & 96.79 & 77.19 \\ \hline \end{tabular}
\end{table}
Table 5: Classification accuracy of the three considered models (%).  
From the confusion matrix shown in Figure 12, it is possible to notice some non-zero off-diagonal elements, which correspond to misclassified letters. In particular, when using Model A, the letter N is incorrectly classified as M in 11% of cases, as S in 14% of cases, and as T in 12% of cases. Such misclassifications are due to the fact that the positions of the fingertips in the letters M, N, S, and T are similar. Since it uses only the fingertip positions, Model A is not robust for capturing the differences between these letters. On the other hand, Model B, which employs a reconstruction of the entire hand by the kinematic model, allows for a more robust classification. In fact, from Figure 13, it can be seen that the letter N is misclassified in only 5% of the cases when using Model B.  
Overall, the results demonstrate the feasibility of the proposed machine learning approach. In particular, it is shown that an SVM classification model can be trained on data from a large publicly-available dataset of images, which are processed by a hand tracking algorithm (MediaPipe Hands) and then successfully tested on a completely different system, the MPS. Thus, the proposed method demonstrated a high generalization capability, which results in a fundamental robustness to environmental factors.  
Furthermore, results from Model C show that it is possible to recognize hand gestures from arbitrary viewpoints and orientations, using a 3D tracking system such as the MPS. Therefore, the recognition system is more flexible when using Model C. The results are worse than Model A and Model B in terms of accuracy, but the additional flexibility could be exploited also in other hand gesture recognition applications.  
Finally, by comparing the results in Table 5 with those of the benchmark experiment that uses MPS data exclusively, it may be observed that the benchmark results in only a slight increase in accuracy for Models A and B. Therefore, the good generalization property of the proposed method is further demonstrated. Instead, the benchmark results in a considerable accuracy increase for Model C, from 77% to 98%, thanks to the training performed on data from the magnetic positioning system. This increase proves that it is possible to implement a gesture recognition system that is both accurate and flexible, by using the developed MPS exclusively.