## 3 Results  
### EDA After Frame Extraction  
As can be seen from Table 2, after frame extraction and annotation, the training dataset from NTHU-DDD consists of 723,248 rows across nine variables. It features data from 18 subjects, each subject in five scenarios with four conditions, ensuring that there are no missing or duplicate data. Analysis of the data also shows two distinct categories for drowsiness as well as eye annotations, and multiple categories for head and mouth annotations. There were also strong correlations between subject conditions and various annotations. In addition, the nine variables mentioned consisted of the subject number, scenario, condition, image path, frame number, drowsiness annotation, head annotation, eye annotation, and mouth annotation. As additional information, the scenario describes the specific driving scenario under which the data were recorded, such as 'daylight', 'with sunglasses', 'nighttime', 'with glasses', and 'nighttime with glasses'. Then, the condition is the specific state or condition under which the data were captured, for instance, 'nonsleepy','slow blink with nodding', or 'yawning'. Followed by that would be the image path of the saved frame, the frame number, the drowsiness annotation of drowsy or non-drowsy, head annotation of'stillness', 'nodding' or 'looking aside', the eye annotation of'stillness' or'sleepy-eyes', and, finally, the mouth annotation of'stillness', 'talking and laughing', or 'yawning'.  
The evaluation dataset includes 143,482 rows with nine variables, covering four subjects across four scenarios under mixed conditions. Similar to the training dataset, the dataset displays distinct annotation categories. The evaluation dataset has the same amount of variables as the training dataset; however, it only has four subjects and four scenarios of 'glasses', 'noglasses', 'nightglasses', or'sunglasses'. There is only a mixed condition per scenario that may contain up to four conditions, which was mentioned earlier in the training dataset. The test dataset, containing 711,877 rows of data from 14 subjects  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Rows of Data** & **Variables** & **Subjects** & \
\begin{tabular}{c} **Scenarios per** \\ **Subject** \\ \end{tabular} &
\begin{tabular}{c} **Conditions per** \\ **Scenario** \\ \end{tabular} \\ \hline Train & 723,248 & 9 & 18 & 5 & 4 \\ Evaluation & 143,482 & 9 & 4 & 4 & Mixed \\ Test & 711,877 & 6 & 14 & 5 & Mixed \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data results after frame extraction.  
across five scenarios, focuses solely on drowsiness annotations with two distinct states: drowsiness and non-drowsiness. There were no missing or duplicate entries that ensured data integrity. In addition, the test dataset only had six variables since it does not include annotations for the head, mouth, and eyes, where it has 14 subjects, the same five scenarios as the training dataset, as well as mixed conditions per scenario, similar to that of the evaluation dataset.  
The extracted data were comprehensive, ensuring the integrity of the dataset for subsequent analysis. The absence of missing or duplicate entries further underscores the reliability of this dataset. The annotations, particularly those for drowsiness and head, eye, and mouth conditions, exhibited strong correlations with the condition of the subject. This distribution of subjects and scenarios across datasets ensures a diverse and representative sample, thereby setting a solid foundation for model training and evaluation.