¿Puede una monitorización fisiológica detectar carga mental en operadores UAV?

Vollständiger Abstract

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Unmanned Aerial Vehicle (UAV) operating combines visual supervision, manual control, and decision-making, making the operator's mental workload relevant to safety. This paper presents a preliminary validation of an intra-subject repeated-measures protocol to study workload during real indoor flights. Ten sessions were recorded with a certified UAS A1/A3 operator, including 2-min blocks under three conditions: Hover, Easy, and Difficult. The acquisition combined UAV telemetry, wrist-worn physiological signals, and face video, synchronized offline. The results show a coherent progression of perceived workload and kinematic demand across conditions. In addition, a personalized baseline based on EDA and PPG discriminated between Hover and Difficult in unseen sessions, supporting the feasibility of personalized physiological monitoring for UAV operators.