The workload sensing for the human machine interface of Unmanned Air Systems

Abstract

Advanced automated assistance systems leading to a complex interplay of humans and automation have been shown to bring in many cases to new types of human errors or incidents. The solution could be an adequate level of human-machine cooperation with shared authority developing a joint-cognitive system which allows for balancing and optimizing human operators' workload under demanding labor conditions. This will be achieved by developing new concepts that balance operator workload based on the operator actual cognitive state and the environment in which the task is conducted. The intent is to explain affordable methods to evaluate the workload as part of the human factor analysis of the Human Machine Interface (HMI) on Unmanned Air Systems (UAS). In the UAS the HMI goes beyond an assistance system and implements a multi-agent perspective where human and machine agents are in charge of common tasks, assigned to the system as a whole.