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dc.contributor.author | Kunimasa, Shutaro | en |
dc.contributor.author | Seo, Kyoichi | en |
dc.contributor.author | Shimoda, Hiroshi | en |
dc.contributor.author | Ishii, Hirotake | en |
dc.contributor.alternative | 國政, 秀太郎 | ja |
dc.contributor.alternative | 瀬尾, 恭一 | ja |
dc.contributor.alternative | 下田, 宏 | ja |
dc.contributor.alternative | 石井, 裕剛 | ja |
dc.date.accessioned | 2019-01-15T08:08:04Z | - |
dc.date.available | 2019-01-15T08:08:04Z | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 2251-1865 | - |
dc.identifier.uri | http://hdl.handle.net/2433/236029 | - |
dc.description | 6th Annual International Conference on Cognitive and Behavioral Psychology (CBP2017): Mar 6, 2017- Mar 7, 2017, Singapore. | en |
dc.description.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. | en |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | Global Science & Technology Forum (GSTF) | en |
dc.rights | GSTF © 2017. By default, GSTF publishes these articles under a Creative Commons Attribution NonCommercial (CC-BY-NC 3.0) license that allows reuse subject only to the use being non-commercial and to the article being fully attributed (http://creativecommons.org/licenses/by-nc/3.0) to GSTF. Articles funded by certain organizations that mandate publication with a Creative Commons Attribution (CC BY 3.0) license, which may require reuse for commercial purposes are allowed, subject to the article being fully attributed to GSTF. | en |
dc.subject | Intellectual Productivity | en |
dc.subject | Machine Learning | en |
dc.subject | Pyshiological Indices | en |
dc.subject | Pupil Diameter | en |
dc.subject | Heart Rate Variability | en |
dc.title | An Estimation Method of Intellectual Work Performance by Using Physiological Indices | en |
dc.type | conference paper | - |
dc.type.niitype | Conference Paper | - |
dc.identifier.jtitle | 6th Annual International Conference on Cognitive and Behavioral Psychology | - |
dc.identifier.volume | 6 | - |
dc.identifier.spage | 111 | - |
dc.identifier.epage | 117 | - |
dc.relation.doi | 10.5176/2251-1865_CBP17.35 | - |
dc.textversion | publisher | - |
dc.address | Graduate School of Energy Science Kyoto University | en |
dc.address | Graduate School of Energy Science Kyoto University | en |
dc.address | Graduate School of Energy Science Kyoto University | en |
dc.address | Graduate School of Energy Science Kyoto University | en |
dcterms.accessRights | open access | - |
datacite.awardNumber | 23360257 | - |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName.alternative | Japan Society for the Promotion of Science (JSPS) | en |
出現コレクション: | 学術雑誌掲載論文等 |
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