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.

Keywords:

• Drone pilot training
• human-drone interaction
• flight simulation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Fatemeh Dalilian

Fatemeh Dalilian is an Industrial and Systems Engineering doctoral candidate at the University of Iowa. Her research focuses on human decision-making and cognitive ergonomics in human-machine interaction, and she develops models and methods to support human cognition and behavior in complex systems.

David Nembhard

David Nembhard is Professor of Industrial and Systems Engineering and Professor of Business Analytics at the University of Iowa. He researches workforce engineering and human cognition as it relates to human performance in complex systems, the science of learning and behavioral operations.