Mental workload assessment: A feature selection approach using physiological and simulated data

Abstract

When a user of a computer system is performing more than one task at the same time, the error rate in the execution of the task increases drastically. In any system this is a critical issue, since the goal of the task is not likely to be met. In that sense, the purpose of mental workload assessment is to estimate the mental demand of tasks to take action according to that, avoiding execution errors. In this paper we study two techniques of mental workload assessment, physiological signals and simulation models of mental behavior with the ACT-R cognitive architecture. The contributions of this study are two: validate a positive correlation among physiological and simulated data and, to develop supervised models of classification with a cost-sensitive feature selection (CFS) algorithm using the ACT-R simulated data as an input of the model. Results indicate that this model selects features of less cost and classify better than a baseline model with 93.1% accuracy in average.