Biometrically Measured Affect for Screen-Based Drone Pilot Skill Acquisition

Abstract

Drones have been used increasingly to aid in Industry 4.0 activities including inspection of the nation’s infrastructure. We investigate several potential underlying affective behaviors related to drone pilot skill acquisition, with the eventual goal of developing methods to enhance human performance. We employ Electroencephalography (EEG) and Eye tracking instrumentation to measure human affect in a series of simulated drone piloting experiments to examine performance using behavioral variables, controller input variables, as well as measures of individual cognitive ability. Current results show that task difficulty impacts the performance/learning process and varies by the nature of the task. The behavioral and biometric measures associated with performance/learning varied significantly among activities. We conclude that drone specifications and training requirements can and should be calibrated to the drone mission. In addition to developing specifications and training requirements, psychological and behavioral measures can also serve as theoretical foundations for modeling complex tasks.