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

doi:10.1007/978-3-030-11051-2_19

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English
Title of host publication	Intelligent Human Systems Integration 2019 - Proceedings of the 2nd International Conference on Intelligent Human Systems Integration IHSI 2019
Subtitle of host publication	Integrating People and Intelligent Systems, 2019
Editors	Tareq Ahram, Waldemar Karwowski
Publisher	Springer Verlag
Pages	123-129
Number of pages	7
ISBN (Print)	9783030110505
DOIs	https://doi.org/10.1007/978-3-030-11051-2_19
Publication status	Published - 2019
Externally published	Yes
Event	2nd International Conference on Intelligent Human Systems Integration, IHSI 2019 - San Diego, United States Duration: 2019 Feb 7 → 2019 Feb 10

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	903
ISSN (Print)	2194-5357

Conference

Conference	2nd International Conference on Intelligent Human Systems Integration, IHSI 2019
Country/Territory	United States
City	San Diego
Period	19/2/7 → 19/2/10

Keywords

Comprehension state
Pupil size
Word association task

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science(all)

Access to Document

10.1007/978-3-030-11051-2_19

Cite this

Minami, K., Watanuki, K., Kaede, K., & Muramatsu, K. (2019). Pupil size as input data to distinguish comprehension state in auditory word association task using machine learning. In T. Ahram, & W. Karwowski (Eds.), Intelligent Human Systems Integration 2019 - Proceedings of the 2nd International Conference on Intelligent Human Systems Integration IHSI 2019: Integrating People and Intelligent Systems, 2019 (pp. 123-129). (Advances in Intelligent Systems and Computing; Vol. 903). Springer Verlag. https://doi.org/10.1007/978-3-030-11051-2_19

Pupil size as input data to distinguish comprehension state in auditory word association task using machine learning. / Minami, Kosei; Watanuki, Keiichi; Kaede, Kazunori et al.
Intelligent Human Systems Integration 2019 - Proceedings of the 2nd International Conference on Intelligent Human Systems Integration IHSI 2019: Integrating People and Intelligent Systems, 2019. ed. / Tareq Ahram; Waldemar Karwowski. Springer Verlag, 2019. p. 123-129 (Advances in Intelligent Systems and Computing; Vol. 903).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Minami, K, Watanuki, K, Kaede, K & Muramatsu, K 2019, Pupil size as input data to distinguish comprehension state in auditory word association task using machine learning. in T Ahram & W Karwowski (eds), Intelligent Human Systems Integration 2019 - Proceedings of the 2nd International Conference on Intelligent Human Systems Integration IHSI 2019: Integrating People and Intelligent Systems, 2019. Advances in Intelligent Systems and Computing, vol. 903, Springer Verlag, pp. 123-129, 2nd International Conference on Intelligent Human Systems Integration, IHSI 2019, San Diego, United States, 19/2/7. https://doi.org/10.1007/978-3-030-11051-2_19

Minami K, Watanuki K, Kaede K, Muramatsu K. Pupil size as input data to distinguish comprehension state in auditory word association task using machine learning. In Ahram T, Karwowski W, editors, Intelligent Human Systems Integration 2019 - Proceedings of the 2nd International Conference on Intelligent Human Systems Integration IHSI 2019: Integrating People and Intelligent Systems, 2019. Springer Verlag. 2019. p. 123-129. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-11051-2_19

Minami, Kosei ; Watanuki, Keiichi ; Kaede, Kazunori et al. / Pupil size as input data to distinguish comprehension state in auditory word association task using machine learning. Intelligent Human Systems Integration 2019 - Proceedings of the 2nd International Conference on Intelligent Human Systems Integration IHSI 2019: Integrating People and Intelligent Systems, 2019. editor / Tareq Ahram ; Waldemar Karwowski. Springer Verlag, 2019. pp. 123-129 (Advances in Intelligent Systems and Computing).

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abstract = "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{\textquoteright}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.",

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author = "Kosei Minami and Keiichi Watanuki and Kazunori Kaede and Keiichi Muramatsu",

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AU - Kaede, Kazunori

AU - Muramatsu, Keiichi

PY - 2019

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AB - 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.

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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