DOI: 10.5176/2251-1865_CBP17.35

Authors: Shutaro Kunimasa, Kyoichi Seo, Hiroshi Shimoda and Hirotake Ishii

Abstract: 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.

Keywords: Productivity; Machine Learning; Pyshiological Indices; Pupil Diameter; Heart Rate Variability

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