Simulator-based, Machine Learning-modelled, Psychophysiological Measurement-augmented, Pilot Screening in the Republic of Singapore Air Force

The project uses a simulator-based screening approach aimed at screening for pilot potential in the Republic of Singapore Air Force (RSAF). Against the backdrop of a shrinking talent pool and lean resources, the simulator-based pilot screening (SBPS) aims to leverage simulators, data science and other emerging technology to enhance effectiveness and optimise efficiency for pilot screening in the RSAF. SBPS uses simulator-based screeners to assess candidates over ten days, through four standardised simulated training/mission sorties. Primary assessments include standardised task performance and behaviour-based observations by RSAF Qualified Flying Instructors (QFIs) and Aviation Psychologists (AvPsychs), as well as objective mission and task performance parameters measured by the simulator. Psychophysiological measures (PPMs), eye trackers, electroencephalograms (EEG), electrocardiograms (ECG), video-based emotion coding, as well as simulator data were explored as means to augment task and behaviour-based assessments.