Authors:
Kaku Kimura
;
Shutaro Kunimasa
;
You Kusakabe
;
Hirotake Ishii
and
Hiroshi Shimoda
Affiliation:
Graduate School of Energy Science, Kyoto University, Kyoto and Japan
Keyword(s):
Intellectual Concentration States, Classification Learning, Physiological Indices, Pupil Diameter, Heart Rate Variability.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
Although modern society has improved the value of intellectual work, its objective and quantitative evaluation method has not been established. In this study, the authors have focused on physiological indices such as pupil diameter and heart rate variability which are supposed to be influenced by their cognitive load in office work, and an estimation method of intellectual concentration states from the measured indices has been proposed. The concentration states to be estimated in this study are one of three states when giving three kinds of cognitive loads which are high, medium and low. As the result of the experiment where intellectual concentration states of 31 participants were estimated, the accuracy was 57.3% in average and it was significantly higher than random estimation (p < 0.001). It was also found that those who had no clear physiological response caused by the difference of cognitive load or those who showed different physiological response when measuring in different
time tended to be low estimation accuracy.
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