A Bayesian Multivariate Approach to Quantifying Pilot Physiology for Adaptive Automation

Abstract

As automation in aviation advances, real-time insights into pilot mental workload (MWL) are critical for adaptive systems. This study applies a Bayesian multivariate approach to quantify MWL using physiological data collected from five instrument-rated pilots during simulated flights. Participants completed four flight scenarios varying in automation level (on/off) and workload (1-back vs. 2-back task), while electroencephalogram (EEG) and electrocardiogram (ECG) data were recorded. A Bayesian model analyzed physiological indicators such as heart rate, heart rate variability, respiration rate, and EEG to understand workload. Results showed significant associations between physiological measures and both automation and workload conditions, despite subjective NASA-TLX scores showing no significant differences. Findings support the use of physiological sensing combined with Bayesian modeling to dynamically assess MWL in small sample contexts. This approach may inform future adaptive automation systems by enabling real-time, individualized workload monitoring to maintain performance and engagement across flight conditions.

Keywords

adaptive automation cognitive workload physiological data human automation interaction aviation

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