Evaluation and prediction mental workload in user interface of maritime operations using eye response

Abstract

Eye response measurement is one of the objective measure methods and useful for assessing of operators' mental workload (MWL). The main objectives of this paper are to consider the relationship between operators' MWL and eye responses in the task of operating marine engine interface. Also, an artificial neural network (ANN) model was developed to predict the operators' MWL based on integrating eye response data. Eye response indices (pupil dilation, blink rate, fixation rate, and saccadic rate) were recorded, and two subjective rating methods (The National Aeronautics and Space Administration's Task Load Index [NASA-TLX] and subjective workload assessment technique [SWAT]) were used for 27 participants. The results again confirm that the eye response is sensitive to MWL in workload levels of the task when using the interface control. The ANN model developed by measuring these indices can predict the operators' MWL with the determination coefficient (R2) of 0.971, 0.912 and 0.918 for training, validation, and testing, respectively. These results indicated that the ANN approach is quite accurate for the prediction of operators' MWL based on eye response indices. Relevance to industryThe developed model is expected to provide the operator with a reference value of their MWL by evaluating their physiological indices. This result might be applied for developing an intelligent prediction model in the actual work environment to inform or support the operator in a variety of ways. From this, the manager can organize the human resources for each task to sustain the appropriate MWL as well as to improve the work performance.