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

Yusuke Osawa, Keiichi Watanuki, Kazunori Kaede, Keiichi Muramatsu

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルIntelligent Human Systems Integration 2019 - Proceedings of the 2nd International Conference on Intelligent Human Systems Integration IHSI 2019
ホスト出版物のサブタイトルIntegrating People and Intelligent Systems, 2019
編集者Tareq Ahram, Waldemar Karwowski
出版社Springer Verlag
ページ155-161
ページ数7
ISBN(印刷版)9783030110505
DOI
出版ステータスPublished - 2019
外部発表はい
イベント2nd International Conference on Intelligent Human Systems Integration, IHSI 2019 - San Diego, United States
継続期間: 2019 2 72019 2 10

出版物シリーズ

名前Advances in Intelligent Systems and Computing
903
ISSN(印刷版)2194-5357

Conference

Conference2nd International Conference on Intelligent Human Systems Integration, IHSI 2019
国/地域United States
CitySan Diego
Period19/2/719/2/10

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンス(全般)

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