## 4 Discussion  
Previous studies show that feature extraction plays a critical role in the development of effective drowsiness detection systems. Integrating the use of facial landmarks with pairwise differences across various models enhances the accuracy of drowsiness detection. The research also demonstrated different feature extraction techniques and model performances. However, the variations in model performance and size indicate the importance of considering computational resources and practical development scenarios. The use of SVM and XGBoost models is the ideal option that offers a balanced trade-off between accuracy and model size, making it ideal for applications in resource-constrained environments, such as the Jetson Nano.  
The comparative analysis between the HTDBN and the proposed models reveals a marginal difference in accuracy but a significant variance in model size, underscoring a pivotal consideration for the deployment of drowsiness detection systems in real-world scenarios. This variance highlights the critical balance between achieving a high accuracy and maintaining a manageable model size for practical applications, particularly in environments with limited computational resources. The efficiency of the proposed model, with its relatively small size and competitive accuracy, suggests its suitability for integration into resource-constrained devices. This finding aligns with the broader objective of developing drowsiness detection technologies that are not only effective but also feasible for widespread implementation. As we move forward, addressing the limitations identified in this research, including dataset diversity and real-world testing conditions, is crucial.  
In recognition of the potential adaptability of the proposed method to low-power devices such as the Jetson Nano, further optimization would be to enhance computational efficiency without compromising accuracy. Future efforts will focus on pilot testing edge devices to ensure robust performance under various environmental constraints and validate their practical applicability. Continuous improvements are crucial as technology advances, ensuring that the proposed method remains effective and relevant. These steps are essential for transitioning from theoretical models to practical applications that can be deployed widely, particularly in resource-constrained environments.  
Moreover, the limitations of this study, including the composition of the dataset and its potential biases, may impact the generalizability of the findings. The models were tested under controlled conditions, which may not fully replicate the real-world scenarios. To address these limitations, future work should focus on expanding the dataset diversity and testing models under dynamic conditions. Exploring advanced machine learning techniques and refining feature extraction methods are crucial for further improving the accuracy and efficiency of the system. The results of this study lay a strong foundation for future studies, which can be enhanced by using more data, exploring hybrid models, and conducting real-world testing under various lighting conditions and with diverse populations. This approach will not only enhance the accuracy and robustness of drowsiness detection systems but also provide insights for other applications requiring fatigue or attention monitoring. The findings and their implications should be discussed in the broadest context possible, considering the perspective of previous studies and working hypotheses and highlighting future research directions.