Saccadic peak velocity as an alternative index of operator attention: A short review

Abstract

IntroductionAutomation research has identified the need to monitor operator attentional states in real time as a basis for determining the most appropriate type and level of automated assistance for operators doing complex tasks. ObjectiveThe development of a methodology that is able to detect on-line operator attentional state variations could represent a good starting point to solve this critical issue. ResultsWe present a short review of the literature on different indices of attentional state and discuss a series of experiments that demonstrates the validity and sensitivity of a specific eye movement index: saccadic peak velocity (PV). PV was able to detect variations in mental state while doing complex and ecological tasks, ranging from air traffic control simulated tasks to driving simulator sessions. ConclusionThis research could provide several guidelines for designing adaptive systems (able to allocate tasks between operators and machine in a dynamic way) and early fatigue-and-distraction warning systems to reduce accident risk.