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

Yoshihiro Banchi, Takashi Kawai, Nagakazu Tomino, Tomohiro Yamagata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 21st Congress of the International Ergonomics Association, IEA 2021 - Healthcare and Healthy Work
EditorsNancy L. Black, W. Patrick Neumann, Ian Noy
PublisherSpringer Science and Business Media Deutschland GmbH
Pages504-507
Number of pages4
ISBN (Print)9783030746100
DOIs
Publication statusPublished - 2021
Event 21st Congress of the International Ergonomics Association, IEA 2021 - Virtual, Online
Duration: 2021 Jun 132021 Jun 18

Publication series

NameLecture Notes in Networks and Systems
Volume222 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

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

Keywords

  • Data augmentation
  • Deep learning
  • Human state recognition

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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