# WorkloadGPT: A Large Language Model Approach to Real-Time Detection of Pilot Workload
## 6 Discussion
### Limitations and Future Work
#### 6.4.2 Future Work  
1. **Expansion of Participant Pool:** Future research will involve recruiting a larger and more diverse group of participants. By including pilots with different levels of experience, from various regions, and with different physiological characteristics, the model can be trained on a more comprehensive dataset. This approach is expected to enhance the model's generalization across different pilot populations and improve its overall accuracy.
2. **Advanced Data Augmentation Techniques:** To further address the limitations of the current data augmentation methods, future work will explore more sophisticated techniques such as variational autoencoders (VAEs) [77] and adversarial training [78]. These methods could create richer and more varied synthetic data that better represent the nuances of pilot workload, ultimately leading to a more robust model.
3. **Mitigation of Catastrophic Forgetting:** To tackle the problem of catastrophic forgetting, future iterations of WorkloadGPT will incorporate continual learning strategies. Techniques such as elastic weight consolidation (EWC) [79] and memory-aware  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Authors** & **Data Type** & \begin{tabular}{c} **Feature** \\ **Selection** \\ \end{tabular} & \begin{tabular}{c} **Classification** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Sample** \\ **Size** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Categories** \\ \end{tabular} & \begin{tabular}{c} **Real-** \\ **Time** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Participants** \\ \end{tabular} & **ACC** \\ \hline \begin{tabular}{c} [PERSON] \\ et al. [76] \\ \end{tabular} & EDA, ECG, RESP & ANOVA & RF, SVC, MLP & 3600 & 2 & No & 90 & 83\% \\ \begin{tabular}{c} [PERSON] et al. [6] \\ [PERSON] et al. [7] \\ \end{tabular} & HRV, GSR & Pearson-\(r\) & ANN & 816 & 3 & No & 20 & 73\% \\ \begin{tabular}{c} [PERSON] et al. [7] \\ \end{tabular} & EEG & \
\begin{tabular}{c} DWT \\ an ensemble \\ structure \\ consisting of \\ several CNNs \\ \end{tabular} & 4680 & 2 & Yes & 26 & 78\% \\ Proposed method &
\begin{tabular}{c} Eye tracking, \\ pressure \\ \end{tabular} & WorkLoadGPT & WorkLoadGPT & 1073 & 3 & Yes & 20 & 87.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of Different Studies.  
synapses (MASs) [80] can be applied to protect previously learned knowledge while allowing the model to adapt to new data.
4. **Enhancement of Instruction Understanding:** The introduction of instruction vectors (IVs) [81] will be refined and optimized. By maintaining and possibly expanding the computational graph related to the IVs during training, the model can improve its ability to retain and correctly apply instructions from prior tasks when learning new ones. Additionally, experimenting with different architectures that better integrate IVs might reduce instances of misinterpretation.
5. **Incorporation of Real-World Scenarios:** Future work will also involve testing the model in more complex and dynamic real-world scenarios, possibly integrating with live flight data. This will help assess the model's performance in practical applications and provide valuable feedback for further development.