Eye-Tracking Measures in Aviation: A Selective Literature Review

Abstract

Objective: The aim of this article is to present a comprehensive review of eye-tracking measures and discuss different application areas of the method of eye tracking in the field of aviation. Background: Psychophysiological measures such as eye tracking in pilots are useful for detecting fatigue or high-workload conditions, for investigating motion sickness and hypoxia, or for assessing display improvements and expertise. Method: We review the uses of eye tracking on pilots and include eyetracking studies published in aviation journals, with both a historical and contemporary view. We include 79 papers and assign the results to the following three categories: Human performance, aircraft design, health and physiological factors affecting performance. We then summarize the different uses of eye tracking in each category and highlight metrics which turned out to be useful in each area. Our review is complementary to that of Ziv (2016). Results: On the basis of these analyses, we propose useful application areas for the measurement of eye tracking. Eye tracking has the potential to be effective in terms of preventing errors or injuries by detecting, for example, fatigue or performance decrements. Applied in an appropriate manner in simulated or real flight it can help to ensure optimal functioning of man-machine systems. Conclusion: Further aviation psychology and aerospace medicine research will benefit from measurement of eye movements.

The Use of Eye Tracking in Assessing Visual Attention

“…Studies moved to how SEM could be improved through training. Additional studies focused on the coordination between head, hand and eye movements and the variation between visual performance and hand performance as indicators of situational awareness (Peißl et al, 2018).…”

“…To encapsulate the developments in the field, (Peißl et al, 2018) carried out a systematic review to reveal eye-tracking measures and how eye tracking is applied in the aviation industry. The study reviewed 79 papers from peer-reviewed journals drawn from the PsychINFO and Science databases.…”

“…In this scenario, the timestamp of eye data and dynamic display data can be synchronized and aligned spatially and the knowledge obtained used to improve aviation outcomes (Wee et al, 2019). Reynal et al (2016) 16 pilots (8 Captains and 8 First Officers) Pertech Reynal et al (2017) 10 airline captains Pertech Dehais et al (2017) 12 participants (captains and first officers) Pertech Peysakhovich et al 2018-- Lounis et al (2019) 12 pilots Smart Assessment Wang and Sun 2013-FaceLAB Maxi and Stein (2013) 16 student pilots Dikablis Sibley et al (2015) 15 volunteers from the Naval Research Laboratory Tobii Li et al (2016) 35 participants (avionics engineers and pilots) ASL Peißl et al (2018) 79 papers from peer-reviewed journals - Guo et al (2018) 20 participants SVS Noelle et al (2018) 79 Navy and Marine Corps student pilots Gazepoint Table 1 summarizes some key features of the studies investigated in this review. These include the model of the eye tracker utilized and the number of participants used in the experimental study.…”

“…The ability to process the visual data is fundamental to pilot performance and aviation safety. The examination of eye movement data has emerged as a reliable tool for mapping eye scanning behavior and determining correlations with cognitive and psychological functions (Peißl et al, 2018). One of the tools that have emerged is the eye tracking technology which can allow users to monitor personnel performance and provide insights into the attentional distribution and underlying decisional processes in pilots (Peysakhovich et al, 2018).…”

Adaptive Neuro-Fuzzy Fusion of Multi-Sensor Data for Monitoring a Pilot’s Workload Condition

“…Based on the research results in the fields of physiology, medicine, ergonomics, etc. [40,41,42,43], the above-mentioned physiological characteristic parameters can be divided into the following two categories according to the change relations with workload. The first category contains the parameters positively related to workload level, namely heart rate ( x 4 ), heart rate variability ( x 5 ), respiratory rate ( x 6 ), pupil diameter ( x 8 ), and gaze time ( x 9 ).…”