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Pupil Size as Input Data to Distinguish Comprehension State in Auditory Word Association Task Using Machine Learning

  • Kosei MinamiEmail author
  • Keiichi Watanuki
  • Kazunori Kaede
  • Keiichi Muramatsu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 903)

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.

Keywords

Pupil size Comprehension state Word association task 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kosei Minami
    • 1
    Email author
  • Keiichi Watanuki
    • 1
  • Kazunori Kaede
    • 1
  • Keiichi Muramatsu
    • 1
  1. 1.Graduate School of Science and EngineeringSaitama UniversitySaitamaJapan

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