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 evaluates task performance of
intellectual workers by using several physiological indices (pupil
diameter and heart rate variability) has been proposed. As
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 evaluation.