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 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 (R 2) 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.
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This study was supported by JSPS KAKENHI Grant Number 23360257.
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Kunimasa, S., Seo, K., Shimoda, H., Ishii, H. (2018). A Trial of Intellectual Work Performance Estimation by Using Physiological Indices. In: Baldwin, C. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2017. Advances in Intelligent Systems and Computing, vol 586. Springer, Cham. https://doi.org/10.1007/978-3-319-60642-2_29
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