Application of Deep Learning for Ergonomic Data Augmentation and Human State Recognition

Yoshihiro Banchi*, Takashi Kawai, Nagakazu Tomino, Tomohiro Yamagata

*この研究の対応する著者

研究成果: Conference contribution

抄録

In ergonomic experiments, a small number of participants is often a problem because a sufficient amount of data is not obtained. In recent years, human state recognition is wide-spread, and estimating the human state from biological information acquired from a wearable device, is useful for improving living behavior. While it is necessary to collect a sufficient amount of data in order to perform state estimation with a certain degree of accuracy, collecting the amount of data requires a considerable cost. This study attempted to expand physiological and psychological data using deep learning. Specifically, information on physiological indicators was added to ACGAN. From the verification using the actual experimental results, it was found that the accuracy of recognizing the human state was improved by using the augmented data compared to the case of learning with a small number of original data.

本文言語English
ホスト出版物のタイトルProceedings of the 21st Congress of the International Ergonomics Association, IEA 2021 - Healthcare and Healthy Work
編集者Nancy L. Black, W. Patrick Neumann, Ian Noy
出版社Springer Science and Business Media Deutschland GmbH
ページ504-507
ページ数4
ISBN(印刷版)9783030746100
DOI
出版ステータスPublished - 2021
イベント 21st Congress of the International Ergonomics Association, IEA 2021 - Virtual, Online
継続期間: 2021 6 132021 6 18

出版物シリーズ

名前Lecture Notes in Networks and Systems
222 LNNS
ISSN(印刷版)2367-3370
ISSN(電子版)2367-3389

Conference

Conference 21st Congress of the International Ergonomics Association, IEA 2021
CityVirtual, Online
Period21/6/1321/6/18

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 信号処理
  • コンピュータ ネットワークおよび通信

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