Decision Tree-Based Pilot Workload Prediction Through Optimized HRV Features Selection

This research explores the use of physiological signals derived from heart activity to assess mental effort during flight-related tasks. Data were collected through wearable sensors during simulations with varying cognitive demands. Specific indicators related to heart rate variability (HRV) were extracted and tested in different combinations to identify those most relevant for distinguishing levels of mental workload (WL). A Random Forest (RF) ensemble method is applied to classify two conditions, and its performance is examined under various settings, including model complexity and data partitioning strategies. Results showed that certain feature pairs significantly enhanced classification accuracy. The best features settings obtained from the RF are then used to train the other two decision trees-based classifiers, namely the AdaBoost and the XGBoost. Moreover, the decision trees models output is compared with predictions from a Kriging spatial interpolation technique, showing superior results in terms of reliability and consistency. This study highlights the potential of using heart-based physiological data and advanced classification techniques for developing intelligent support systems in aviation.