Charting the Edges of Human Performance

In the Horizon 2020 funded Future Sky Safety programme, the Human Performance Envelope project pushed airline pilots to the edges of their performance in real-time cockpit simulations, by increasing stress and workload, and decreasing situation awareness. The aim was to find out how such factors interact, and to detect the edges of human performance where some form of automation support should be employed to ensure safe continued flight. A battery of measures was used, from behavioural to physiological (e.g. heart rate, eye tracking and pupil dilation), to monitoring pilot performance in real time. Several measures – e.g. heart rate, heart rate variability, eye tracking, cognitive walkthrough, and Human Machine Interface (HMI) usability analysis – proved to be useful and relatively robust in detecting performance degradation, and determining where changes in information presentation are required to better support pilot performance in challenging situations. These results led to proposed changes in a prototype future cockpit human-machine interface, which were subsequently validated in a final simulation. The results also informed the development of a ‘Smart-Vest’ that can be worn by pilots to monitor a range of signals linked to performance. 1 The Human Performance Envelope The concept of Human Performance Envelope (HPE) considers nine Human Factors that influence performance. The factors include attention, situation awareness, vigilance, teamwork, workload, communication, trust, fatigue, and stress. The aim of the HPE concept is to map how these factors work alone and in an interacting combination and how they lead to a Human Performance (HP) modification. While several indicators, tests, metrics, and tools to measure individual Human Factors have been produced over the years, there is still a need to better assess how to offer precise ways to monitor the combination and interaction of multiple range of factors within a HPE framework. This consideration is especially relevant for complex Human Factors concepts like stress, fatigue and situation awareness. In aviation research these factors are not always represented by univocal metrics, and are investigated by a series of behavioral indicators that mostly focus on cognitive concepts (disregarding the emotional aspects concerning perception and management) or by neurophysiological indexes that require additional analysis to better understand the combination of processes that they could reflect (e.g. autonomic nervous systems modulations). To assess the HPE measurement, a series of experimental trials were set up in the course of the project. Different tasks designed to control and manipulate the levels of three human factors (workload, stress and situation awareness) in a HPE framework, and different configurations of newly developed Human Machine Interfaces (HMI), were manipulated in order to provoke degradation of pilot performance and to measure the impact of the single and combined HPE factors on pilots’ performances. 2 Experimental set-up Two experiments were set up in the project and conducted with professional airline pilots. The first experiment was conducted in an A320 full flight research simulator. The second experiments took place in a static advanced touch-screen concept-cockpit simulator, its flight mode and systems also based on an A320. Various measurements were used in both experiments. They include questionnaires (ISA, NASATLX, SART, SACL), eye-tracking including pupil diameter, physiological sensors (electrocardiogram, respiration, body temperature, 3-axis acceleration, activity), performance curves, behavioural markers, video and voice recordings, simulator data, and benefits questionnaires. An analysis of mental representation and the use of a developed competency assessment tool provided further data for the analysis. The first experiments were conducted in an A320 research simulator called AVES (Air VehiclE © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/). MATEC Web of Conferences Volume 304, 2019, Article number 06007 DOI:10.1051/matecconf/201930406007EASN 2019