Assessment of Ocular and Physiological Metrics to Discriminate Flight Phases in Real Light Aircraft

• Published in: Human factors 2018-11, Vol.60 (7), p.922-935
• Main Authors: Scannella, Sébastien, Peysakhovich, Vsevolod, Ehrig, Florian, Lepron, Evelyne, Dehais, Frédéric
• Format: Article
• Language: English
• Subjects: Accident analysis; Aircraft; Aircraft accidents; Aircraft accidents & safety; Classifiers; Cockpits; Cognitive ability; Data processing; Flight; Flight conditions; Flight safety; Flight simulators; Forensic engineering; Heart; Landing behavior; Light aircraft; Phases; Pilot training; Pilots; Saccadic eye movements; Safety; Space life sciences; Statistical analysis; Tracking devices; Tracking equipment; Workload; Workloads
• ISSN: 0018-7208; 1547-8181
• DOI: 10.1177/0018720818787135

Objective: The purpose of the present study was to find psychophysiological proxies that are straightforward to use and could be implemented in actual flight conditions to accurately discriminate pilots’ workload levels. Background: Piloting an aircraft is a complex activity where cognitive limitations may jeopardize flight safety. There is a need to implement solutions to monitor pilots’ workload level to improve flight safety. There has been recent interest in combining psychophysiological measurements. Most of these studies were conducted in flight simulators at the group level, limiting the interpretation of the results. Methods: We conducted an experiment with 11 pilots performing two standard traffic patterns in a light aircraft. Five metrics were derived from their ocular and cardiac activities and were evaluated through three flight phases: takeoff, downwind, and landing. Results: Statistical analyses showed that the saccadic rate was the most efficient metric to distinguish between the three flight phases. In addition, a classifier trained on the ocular data collected from the first run predicted the flight phase within a second run with an accuracy of 75%. No gain in the classifier accuracy has been found by combining cardiac and ocular metrics. Conclusions: Ocular-based metrics may be more suitable than cardiac ones to provide relevant information on pilots’ flying activity in operational settings. Applications: Electrocardiographic and eye-tracking devices could be implemented in future cockpits as additional flight data for accident analysis, an objective pilot’s state evaluation for training, and proxies for human-machine interactions to improve flight safety.