## 4 Results and Discussion
### Automatic Hand Gesture Recognition  
The results obtained from our analysis are truly remarkable, as illustrated by the confusion matrices in Figure 9, for both RGB-only and RGB-D acquisition processes. The algorithm was tested on Windows 11 on an Intel(R) Core(TM) i5-9300H CPU @ 2.40 GHz and an NVIDIA GeForce GTX 1660 Ti. Both the RGB and RGB-D devices have demonstrated exceptional performance, showcasing their effectiveness in accurately classifying the data. The matrix highlights the high levels of precision and recall achieved, indicating a strong capability in distinguishing between classes. The capability of both the models to classify different hand signs with great accuracy is also proved by computing significant metrics, such as balanced accuracy and f1 m score [48]. Balanced accuracy takes into account both sensitivity (true positive rate) and specificity (true negative rate), providing a more comprehensive view of a model's performance across different classes. It mitigates the bias that can arise when one class dominates the dataset, ensuring that the model is assessed fairly, regardless of class distribution. The F1 m score, which is a variant of the F1 score that can accommodate multiple classes, offers a harmonic mean of precision and recall. This balance is crucial when dealing with imbalanced datasets, as it emphasizes both the ability to correctly identify positive instances and the minimization of false positives. According to the achieved results, the algorithm working with RGB images achieved 99.83% of balanced accuracy and 99.84% regarding F1 m score. On the other hand, RGB-D images allow for 99.98% and 99.98% concerning balanced accuracy and F1 m score, respectively. This achievement underscores the robustness of our approach, that further  
Figure 8: Hangman Game screenshots. overcome the already remarkable results obtained in the same field [20], and the potential for both the categories of devices. The excellent performance achieved on the dataset has been similarly observed in the real-time case study described in the following paragraph. While some minor delays were noted during the application execution, it is important to emphasize that these delays are primarily attributable to the hardware used, such as the reliance on a USB 2.0 port instead of a USB 3.0 connection for linking the external camera to the PC. Despite these small setbacks, the overall results reaffirm the effectiveness of our approach, demonstrating that the high accuracy and reliability seen in the dataset translate well into real-world applications. Although the application is designed for usage in a moderately controlled setting favorable for learning, further qualitative tests were performed to check the model's generalizability and adaptability under uncontrolled situations. Specifically, the application was tested indoors and outdoors under boundary circumstances such as fuzzy backdrops, suboptimal illumination, and varying hand orientation and distance from the camera. Figure 10 illustrates some of the obtained results, which allowed us to demonstrate the system's proper functioning, even under less controlled conditions, and its potential range of usage in various scenarios. The application operated in real-time on a qualitative level, but a more accurate analysis has been carried out to quantitatively assess the latency added for automatic gesture recognition. A human being has a reaction time that is around 150 ms [49]. We assumed that latency would be calculated by adding the processing times of the MediaPipe hand landmark library and the classification algorithm. On a GPU, the former can achieve  
Figure 10: Qualitative tests in real-world uncontrolled settings. Different colors refer to different subgroups of hand landmarks. Figure 9: Confusion matrices. a latency of approximately 12 ms [37]. The latter was tested and was determined to have a maximum latency of 12 ms on our GPU.