University of Hyogo Academic Staff search

Takayuki Fujita

  (藤田 孝之)

Profile Information

Affiliation
Professor (Deputy Director), Advanced Medical Engineering Research Institute, University of Hyogo
Degree
博士(工学)(Mar, 2000, 姫路工業大学)

J-GLOBAL ID
201801005909816068
researchmap Member ID
B000310193

Papers

 178
  • 室井 大和, 藤田 大輔, 小橋 昌司, 藤田 孝之
    バイオメディカル・ファジィ・システム学会年次大会講演論文集, 36回 5-8, Dec, 2023  
  • Takahito Yokota, Kensuke Kanda, Takayuki Fujita, Kazusuke Maenaka
    IEEJ Transactions on Sensors and Micromachines, 143(8) 256-261, Aug 1, 2023  Peer-reviewed
  • Kensuke Kanda, Yoshitaka Kajiyama, Yoshiaki Hirata, Yasuhisa Shimakura, Takayuki Fujita, Kazusuke Maenaka
    IEEJ Transactions on Sensors and Micromachines, 143(6) 137-142, Jun 1, 2023  Peer-reviewed
  • Noriyasu Kondo, Daisuke Fujita, Syoji Kobashi, Takayuki Fujita
    Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 1652-1657, 2023  
    The incidence of falls in hospital facilities is high and can lead to a decrease in patients' quality of life and an increase in medical expenses. Therefore, the development of a system that can predict getting-up from a bed is necessary. This study proposes a get-up detecting sensor using 6-axis inertial sensor. This system can detect getting-up from a bed in real-time using machine learning with Edge AI. To evaluate the basic performance of the proposed system, a protocol was applied for four subjects, and data were collected. Alert accuracy rate and false alert rate were used as evaluation metrics, and a model was built using data from three of the subjects and evaluated with the remaining subject, which was repeated for all four subjects. For high-risk bed-leaving behavior, medium-risk pre-bed-leaving behavior, and low-risk get-up behavior, the alert accuracy rate (i.e., Recall) was 89.4%, 97.5%, and 86.3%, respectively, and the false alert rate (1-Precision) was 6.3%, 10.2%, and 0.0%, respectively. This confirmed the possibility of predicting rising behavior with high accuracy. Furthermore, as a proof of concept, a real-time get-up detection system was developed, and its practicality was demonstrated. Future challenges include reevaluating feature extraction and evaluating the performance of the proposed system with a diverse range of subjects of different ages, genders, and health statuses.
  • Noriyasu Kondo, Daisuke Fujita, Syoji Kobashi, Takayuki Fujita
    SMC, 1652-1657, 2023  

Misc.

 133

Books and Other Publications

 5

Presentations

 369

Research Projects

 15

Industrial Property Rights

 19