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Assessing Cognitive Workload in Air Traffic Management using Cardio-Respiratory Sensor: A Performance Evaluation

Chandan Sheikder

Developing Air Traffic Management (ATM) and avionics human-machine framework ideas need real-time surveillance of the human operator to enable unique job assessment and system adaptability characteristics. To implement these advanced notions, a set of sensors capable of consistently and correctly capturing neurophysiological data is required. The scientific verification and performance evaluation of a cardio-respiratory sensor with ATM and avionics applications are presented in this research. The processed physiological measures from the specified commercial device are validated against clinical-grade equipment. Unlike previous studies that just looked at physical effort, this characterization looked at cognitive workload as well, which provides some extra hurdles to cardiorespiratory monitoring. The paper also discusses how to quantify ambiguity in the cognitive and somatic estimation process based on the ambiguity in the supplied cardio-respiratory measures. The sensor validation and uncertainty propagation findings confirm the commercialized cardiorespiratory sensor's fundamental compatibility for the planned aircraft application but emphasize the comparatively low performance within respiratory measures throughout a purely mental task.