## 5 Discussion
### Datasets' Performances Evaluation  
The results presented in Sections 4.1 and 4.2 are quite revealing in several ways. Despite having fewer samples per class compared to the Jester and EgoGesture datasets, the sign-language dataset was still the most effective. This can be attributed to several factors: The images in the dataset have a reasonable resolution, which enhances the clarity and quality of the data for use with the MHL model. The gesture duration in this dataset is consistent, ensuring uniform video sample lengths. Unlike other datasets, which exhibit standard deviations of 0.49, 2.38, and 2.48 s per clip, this dataset maintains a minimal standard deviation of 0.21 s per clip, avoiding overly short or lengthy clips. These details are presented in Table 1.  
Similarly, in this dataset, hand gestures are captured with sharp-edge definition and minimal motion distortion (no blurriness) ensuring a successful detection rate for hand landmarks using the MHL model. This is due to the well-trained subjects who performed the gestures at an optimal speed, neither too fast nor too slow. This judgment was made by carefully analyzing the dataset's image quality and the contents of the image frames. For a cross-dataset comparison, the blurriness index was estimated by analyzing the motion blur levels across frames in each dataset (see Table A1 and Figure A1 in the Appendices A and B). We considered edge sharpness and pixel variance as key indicators, assigning values based on the relative frequency of blurred images. As observed, Jester exhibited the highest blurriness, followed by NVGesture, EgoGesture, and sign-language. This indicates that gestures in certain datasets were performed with less precision, likely due to the lack of proper training among subjects, leading to inconsistencies in movement and reduced model performance. Also, the detection rate of hand landmarks was notably high for the sign-language dataset with the MHL model, contributing to its effectiveness. On the other hand, the Jester dataset, despite having a large number of samples per class, based on the information presented in Table 1, had the following limitations revealed, which make this dataset the least effective:  
The resolution of frames in the Jester dataset is very low, i.e., 100 \(\times\) 120, which is not suitable for the MHL model, resulting in a low detection rate of hand landmarks as shown in Figures 4 and 5. The standard deviation \(\sigma\) of the gesture clips (duration in seconds) reveals that there is a huge difference in the length of the gesture clips in the Jester dataset, as shown in Table 1, resulting in too many random short and long clips and thus eventually leading to redundant data. Similarly, most of the frames in this dataset are not clear, because of the low resolution, as shown in Figure 3. The observed variability and inconsistency in the Jester dataset suggest that such measures were not implemented, potentially affecting the dataset's reliability. Previous studies have highlighted the role of trained subjects in maintaining dataset consistency and quality during gesture recognition tasks [48]. Biases introduced in a dataset play a crucial role in its effectiveness [9]. All three datasets besides sign-language exhibit a bias, as most gestures are performed with the right hand. When exposed to cross-hand testing, model performance declined. The cross-hand testing was employed to highlight a bias in the datasets (NVGesture, Jester, and EgoGesture), as the gestures in these datasets were mostly recorded predominantly using one hand, leading to errors when subjects used their less-practiced hand, affecting recognition accuracy. This lowered the performance as can be seen in the Results Section 4.2, under Tables 4-7. These results show the performance drops of the individual datasets under different test scenarios. It can be seen clearly from the graph showing the evaluation metrics for cross-hand testing, where the three datasets have lower performance (besides the sign-language dataset).