# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 6 Conclusions  
High pilot workload is a major contributing factor to aviation accidents, and workload detection can help reduce the incidence of such accidents. To address the issues in existing workload detection models, such as interference with pilot operations and the lack of real-time applicability, and to enhance the model's prediction accuracy and generalizability, this paper proposed the Adaptive KNN-Ensemble Pilot Workload Detection (AKE-PWD) model. By harnessing low-interference detection metrics, i.e., using the telemetry-based eye-tracking metrics and seat pressure metrics as model inputs, the approach achieves a streamlined detection process without being perceived by the subjects at all. By gathering continuous data from flight simulation experiments and employing 10 s time windows as sampling points, it ensures real-time detection capabilities. By constructing the Adaptive KNN-Ensemble model, which includes an outer KNN and an inner ensemble classifier composed of RF, GBT, and FCN-Transformer, we can identify the cluster of physiological features relevant to the input data and subsequently select the corresponding ensemble model which integrates the strengths of the three base learners. In this way, detection generalizability across different pilots is facilitated. The real-time workload detection across  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** \\ \hline Before & 0.8493 & 0.7470 & 0.7949 \\ After & 0.8497 & 0.7936 & 0.8172 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model performance of level 3 workload before and after incorporating flight ability.  
different pilots achieved an accuracy of 82.6%, surpassing baseline workload detection methods. Additionally, it operates with a running time of less than 0.1 s and can detect the workload every 10 s, ensuring real-time capability. The research findings have broad application prospects. For instance, specific conditions and causes of elevated workload can be identified from the real-time detection data. The findings can guide the design and improvement of the cockpit HMIs and the aircraft crew operation procedures. Additionally, it can provide comprehensive metrics to help aviation regulatory bodies establish standards and guidelines. This provides new insights into the workload of human-machine interaction and has significant potential for the safe, intelligent, and efficient development of future aviation traffic systems.  
Conceptualization, L.Y.; methodology, Y.L., Y.G. and L.Y.; software, Y.G. and J.S.; validation, Y.L., Y.G., J.S. and X.W.; formal analysis, Y.L. and Y.G.; investigation, Y.L., J.S., Y.G. and X.W.; resources, L.Y.; data curation, Y.L. and Y.G.; writing--original draft preparation, Y.L.; writing--review and editing, L.Y. and H.Z.; visualization, X.W.; supervision, L.Y. and H.Z.; project administration, L.Y.; funding acquisition, L.Y. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Natural Science Foundation Project under Grant 52302442.  
The study was approved by the Institutional Ethics Committee of Tongji University for studies involving humans with the approval number tjdxsro82 on 15 April 2024.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The raw data supporting the conclusions of this article will be made available by the authors on request. Due to confidentiality agreements, access to the raw data may be restricted.  
The College of Transportation and Engineering of Tongji University provided all the equipment we need. Our sincere gratitude goes to our college and all the volunteers who actively participated in this research.  
The authors declare no conflicts of interest.