Study on gait discrimination method by deep learning for biofeedback training optimized for individuals

Yusuke Osawa, Keiichi Watanuki, Kazunori Kaede, Keiichi Muramatsu

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

2 Citations (Scopus)

Abstract

In this research, to develop a biofeedback training system where trainees can efficiently train inadequacies that do not satisfy ideal walking using a deep learning, we examine a method that discriminates between ideal walking and nonideal walking. In the experiment, to examine the walking components used for the input data, the ground reaction force and joint angle were measured when young people walked normally and when they walked with a brace, to simulate elderly motions. Further, these data were discriminated between conditions as input data using a Convolution Neural Network (CNN). The average accuracy was 79.5% when all walking components were used as input data. In addition, it is thought that it is most suitable to discriminate walking by using all walking components, in consideration of implementation in the system.

Original languageEnglish
Title of host publicationIntelligent Human Systems Integration 2019 - Proceedings of the 2nd International Conference on Intelligent Human Systems Integration IHSI 2019
Subtitle of host publicationIntegrating People and Intelligent Systems, 2019
EditorsTareq Ahram, Waldemar Karwowski
PublisherSpringer Verlag
Pages155-161
Number of pages7
ISBN (Print)9783030110505
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event2nd International Conference on Intelligent Human Systems Integration, IHSI 2019 - San Diego, United States
Duration: 2019 Feb 72019 Feb 10

Publication series

NameAdvances in Intelligent Systems and Computing
Volume903
ISSN (Print)2194-5357

Conference

Conference2nd International Conference on Intelligent Human Systems Integration, IHSI 2019
Country/TerritoryUnited States
CitySan Diego
Period19/2/719/2/10

Keywords

  • Biofeedback training
  • Convolution neural network
  • Ground reaction force
  • Motion capture
  • Walking assistance

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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