Adaptive Human-Machine Interfaces and Interactions (HMI2) are closed-loop cyber-physical systems comprising a network of sensors measuring human, environmental and mission parameters, in conjunction with suitable software for adapting the HMI2 (command, control and display functions) in response to these real-time measurements. Cognitive HMI2 are a particular subclass of these systems, which support dynamic HMI2 adaptations based on the user’s cognitive state. These states are estimated in real-time using various neuro-physiological parameters from gaze, cardiorespiratory and brain signals, which are processed by an Adaptive Neuro-Fuzzy Inference System (ANFIS). However, the accuracy and precision of neuro-physiological measurements are affected by a variety of environmental factors and therefore need to be accurately characterised prior to operational use. This paper describes the characterisation activities performed on two types of eye tracking devices used in the Aerospace Intelligent and Autonomous Systems (AIAS) laboratory of RMIT University to support the development of cognitive human-machine systems. The uncertainty associated with the ANFIS outputs is quantified by propagating the uncertainties in the input data (determined experimentally) through the inference engine. This process is of growing relevance because similar machine learning techniques are now being developed for an increasing number of applications including aerospace, transport, biomedical and defence cyber-physical systems.
