A method for analyzing biometric data to assess the cognitive load and stress resistance of an agricultural unmanned aerial vehicle operator during mission planning

We present a computer vision method to assess cognitive load and stress resistance of an agricultural unmanned aerial vehicle operator during mission planning in virtual reality. The approach combines geometrically rigorous gaze-to-user-interface mapping projecting the eye-tracker ray into widget space to obtain metrically correct hits on areas of interest, behavioral and ocular biomarkers, and image-like attention representations, such as heatmaps and recurrence plots. In a study with twelve participants across four scenarios, we recorded 1,198 interaction events and obtained 85.3 % accuracy of gaze-to-interface hits; with increasing difficulty, fixation durations shortened, transition entropy increased, and event-locked pupil responses became larger and slower to recover. Planning time and the time required for replanning increased, while route quality decreased under time pressure. The approach relies only on software–platform aggregate signals and does not use raw eye images, which supports privacy-preserving deployment and portability to ground control software.