Pilot Workload During Walk-around Inspection Based on HRV Features

The pre-flight walk-around inspection represents a critical safety procedure in civil aviation; however, its cognitive and physiological workload mechanisms remain underexplored. This study systematically investigated pilot workload during standardised SR20 aircraft inspections by integrating heart rate variability (HRV) feature analysis, expert evaluation and task performance assessment. Forty-one flight students, stratified by training and experience levels, participated under controlled real-aircraft conditions. HRV signals were collected using wearable optical sensors, and multiple time-, frequency- and nonlinear-domain features were extracted. Feature selection was conducted via statistical testing and random forest ranking, identifying mean HR, maximum HR, standard deviation of HR, mean NNI and median NNI as the most workload-sensitive indicators. Six deep learning architectures were developed and compared under two feature-processing strategies: complete HRV features and PCA-reduced features. Results revealed a clear gradient of workload across experience groups, with experienced pilots exhibiting the lowest load and highest task performance, whereas untrained participants demonstrated the highest load. In terms of classification, the Depthwise CNN achieved the best overall performance (accuracy = 0.9372) with full HRV features, while the CNN-GRU hybrid was most effective (accuracy = 0.9186) after PCA reduction. These findings highlight the importance of aligning feature dimensionality with model architecture and provide an empirical foundation for establishing objective workload monitoring frameworks in aviation safety management.