Pupil size as input data to distinguish comprehension state in auditory word association task using machine learning

Kosei Minami*, Keiichi Watanuki, Kazunori Kaede, Keiichi Muramatsu

*この研究の対応する著者

研究成果: Conference contribution

抄録

In communication, it is very important for a speaker to understand the comprehension state of the speaking partner. In this study, the “comprehension state” is defined as whether or not the speaker’s message is clearly understood, which is difficult to accurately evaluate. This study aims to evaluate the comprehension state from the pupil size using machine learning. We conduct a word association task using elements that are similar to those used in conversations and measure the pupil size; this pupil size data is used as input data for machine learning. The results show that high accuracy is achieved by learning the low frequency components of the pupil size.

本文言語English
ホスト出版物のタイトルIntelligent Human Systems Integration 2019 - Proceedings of the 2nd International Conference on Intelligent Human Systems Integration IHSI 2019
ホスト出版物のサブタイトルIntegrating People and Intelligent Systems, 2019
編集者Tareq Ahram, Waldemar Karwowski
出版社Springer Verlag
ページ123-129
ページ数7
ISBN(印刷版)9783030110505
DOI
出版ステータスPublished - 2019
外部発表はい
イベント2nd International Conference on Intelligent Human Systems Integration, IHSI 2019 - San Diego, United States
継続期間: 2019 2 72019 2 10

出版物シリーズ

名前Advances in Intelligent Systems and Computing
903
ISSN(印刷版)2194-5357

Conference

Conference2nd International Conference on Intelligent Human Systems Integration, IHSI 2019
国/地域United States
CitySan Diego
Period19/2/719/2/10

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンス(全般)

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