A cognitive load assessment method for fighter cockpit human-machine interface based on integrated multi-criteria decision making

Abstract

The primary interface for communication between pilots and aircraft systems is the fighter cockpit Human-Machine Interface (HMI). Since reduced cognitive load ensures that pilots will operate the aircraft safely and effectively, it is critical to evaluate pilot cognitive load during HMI. A cognitive load assessment method, based on multi-criteria decision-making, is proposed to accurately quantify the relationship between the HMI and the cognitive load in the fighter’s cockpit. Firstly, based on the integrated Multi-Criteria Decision-Making (MCDM) method, the Step-wise Weight Assessment Ratio Analysis (SWARA) and MEthod based on the Removal Effects of Criteria (MEREC) methods are used to assign subjective and objective weights, respectively. Moreover, the Combined Compromise Solution (CoCoSo) method is applied to rank the scenarios to establish a cognitive load assessment model for the cockpit HMI of the fighter jet. Secondly, an evaluation standard system of fighter cockpit HMI is proposed, drawn upon multiple sets of eye-movement criteria and subjective assessment criteria. Moreover, cognitive load experiments of fighter cockpit HMI are conducted using eye-tracking technology to get the objective physiological cognitive data as well as the subjective assessment data of the subjects. Consequently, the parameter data sets of the eye-movement criteria and the subjective criteria for the evaluation of cognitive load are obtained. The proposed method is applied to analyze the cognitive load assessment of a fighter jet cockpit HMI layout. This application aims to verify the effectiveness of the assessment method in evaluating the cognitive load of the HMI layout. Through sensitivity and comparison analyses, the model was further verified to have excellent robustness and applicability for cognitive load assessment. The advantages of this method can be seen through the comparison, which is that it has higher discriminability when assessing the degree of cognitive load. At the same time, it has higher flexibility in dealing with complex and ambiguous cognitive load assessment information.