An Estimation Method of Intellectual Concentration State by Machine Learning of Physiological Indices

Abstract

Although recent information society has improved the value of intellectual work productivity, its objective and quantitative evaluation has not been established. It is suggested that intellectual productivity can be indirectly evaluated by estimating intellectual concentration states when giving cognitive load. In this study, therefore, the authors have focused on physiological indices such as pupil diameter and heart rate which are supposed to be closely related to cognitive load in office work, and an estimation method of intellectual concentration states from the measured indices has been proposed. Multiple patterns of classification learning methods such as Decision Tree, Linear Discrimination, SVM, and KNN were employed as the estimation method. Based on the estimation method, an evaluation experiment was conducted where 31 male university students participated and the measured psychological indices were given to the classification learning estimators.