Presentation
A Bayesian Multivariate Approach to Quantifying Pilot Physiology for Adaptive Automation
SessionAS2: Physiology
DescriptionAs technology becomes increasingly capable, and the question of automation becomes not “if” but “how much”, designers will rely on the human factors of a system to predicate the architecture and theoretical basis of the technology involved. The aviation industry and flight deck information technology have been increasingly affected by the effects of automation introductions in the past 20 years. Traditional performance metrics may be affected as automation takes over tasks typically performed by humans, furthermore subjective questionnaires (e.g. NASA-TLX) are ill suited to aviation applications and have been shown to increase non-linearly as workload increases, taking a sigmoid shaped curve due to the subjectivity of the responses. This study introduces a quantitative approach to model pilot mental workload using real-time physiological indicators, offering insight into adaptive automation implementation based on operator state.
Event Type
Lecture
TimeTuesday, October 14th11:30am - 11:50am CDT
LocationGrand Hall I
Aerospace Systems
Human AI Robot Teaming (AI)