研究者業績

YAMAZOE HIROTAKE

  (山添 大丈)

Profile Information

Affiliation
准教授, 大学院工学研究科 電子情報工学専攻, 兵庫県立大学
Degree
Ph.D. in Engineering(Osaka University)

J-GLOBAL ID
200901050672492802
researchmap Member ID
5000024529

External link

Papers

 33
  • Syunsuke YOSHIDA, Makoto SEI, Akira UTSUMI, Hirotake YAMAZOE
    Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 36(2) 623-630, May 15, 2024  Peer-reviewedLast authorCorresponding author
  • SEI Makoto, UTSUMI Akira, YAMAZOE Hirotake, LEE Joo-Ho
    J106-D(4) 268-276, Apr 1, 2023  Peer-reviewed
    This paper proposes a method of integrating known relations about the target task into a neural network as predefined computational modules. During the training process, the network is automatically induced to estimate the hidden parameters required by the embedded computational modules. In experiments, the proposed method was applied to 2D eyeball center position estimation. Experimental results confirmed that the accuracy of 2D eyeball center position estimation is improved and that the system acquires the ability to estimate 3D eye positions, which are hidden parameters.
  • Hirotake Yamazoe, Jaemin Chun, Youngsun Kim, Kenta Miki, Takuya Imazaike, Yume Matsushita, Joo-Ho Lee
    Intelligent Service Robotics, Nov 8, 2022  Peer-reviewedLead author
  • Makoto Sei, Akira Utsumi, Hirotake Yamazoe, Joo-Ho Lee
    Applied Intelligence, 52(10) 11506-11516, Jan 27, 2022  Peer-reviewed
  • Yume Matsushita, Dinh Tuan Tran, Hirotake Yamazoe, Joo-Ho Lee
    Journal of Computational Design and Engineering, 8(6) 1499-1532, Oct 29, 2021  Peer-reviewed
    Abstract Gait analysis has been studied for a long time and applied to fields such as security, sport, and medicine. In particular, clinical gait analysis has played a significant role in improving the quality of healthcare. With the growth of machine learning technology in recent years, deep learning-based approaches to gait analysis have become popular. However, a large number of samples are required for training models when using deep learning, where the amount of available gait-related data may be limited for several reasons. This paper discusses certain techniques that can be applied to enable the use of deep learning for gait analysis in case of limited availability of data. Recent studies on the clinical applications of deep learning for gait analysis are also reviewed, and the compatibility between these applications and sensing modalities is determined. This article also provides a broad overview of publicly available gait databases for different sensing modalities.

Misc.

 276

Books and Other Publications

 1

Presentations

 225

Teaching Experience

 22

Research Projects

 7