Coordinated by:
1
Deep Blue
Consortium members:
Sapienza, University of Rome, ENAC – the French National School for Civil Aviation
Research Questions
Can the ATCOs’ cognitive states be described and classified by monitoring relevant neurometric and neurophysiological parameters? Can this classification be used for the design of adaptive interfaces?
Research Scope
NINA is based on the assumption that different air traffic controller’s cognitive states lead to differences in physiological activity which are observable via a combination of neurometric and neurophysiological measurements, including brain and cardiac activity. NINA will explore how to describe the ATCOs’ cognitive states by monitoring relevant physiological parameters, including brain activity. These measurements will be carried out in a laboratory setting and in an ATM simulation facility. This will ultimately allow discriminating different cognitive states, such as different workload levels, the development of expertise, different levels of cognitive control (similar to Rasmussen’s Skills-Rules-Knowledge taxonomy). If successful, this can be used as an input to adaptive ATM tools which adjust the interfaces and support functions to the mental state of the air traffic controller. Whilst the use of neurometric and neurophysiological indicators is well established in laboratory settings, NINA attempts to bring these into a more operational context.
Research Results
The expected results of the project are twofold: firstly, the project will assess the feasibility of using neurometric and neurophysiological indicators in order to infer air traffic controllers’ cognitive states; secondly, the project will deliver a set of prototypes and guidelines for HMI adaptive solutions based on such indicators, exploring design opportunities and difficulties.