In order to evaluate the intellectual productivity quantitatively, most
of conventional studies have utilized task performance of cognitive tasks. Meanwhile,
more and more studies use physiological indices which reflect cognitive
load so as to evaluate the intellectual productivity quantitatively. In this study,
the method which estimates task performance of intellectual workers by using
several physiological indices (pupil diameter and heart rate variability) has been
proposed. As the estimation models of task performance, two machine learning
models, Support Vector Regression (SVR) and Random Forests (RF), have been
employed. As the result of a subject experiment, it was found that coefficient of determination (R2) of SVR was 0.875 and higher than that of RF (p＜0.01). The result suggested that pupil diameter and heart rate variability were effective as the explanatory variables and SVR estimation was also effective in task performance
estimation.