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IJMLC 2022 Vol.12(3): 102-106 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2022.12.3.1087

Research on Dynamic Attention State during Cognitive Rehabilitation with Cooking for Patients with Acquired Brain Injury

Sho Ooi, Kazuki Hashimoto, Haruo Noma, and Mutsuo Sano

Abstract—Patients with acquired brain injuries present adverse symptoms such as attention, memory, and functional disorders. They prevent them from effectively executing the activities of daily living. In the rehabilitation of patients with acquired brain injuries, patients need to be aware of their cognitive states. Its effective method is to watch the experience videos and present quantitative cognitive status to patients. Current methods to evaluate cognitive states using require special/specific toolkits, and the methods are rigorous when applied for real-time dynamic evaluation. Moreover, patients are often burdened by the need to undergo tests as required by the evaluation methods. In this paper, we propose a method to evaluate the attention function from the state of handling a kitchen knife that includes dangerous movements even during cooking. As a result, we defined four attention levels during cutting behavior in cooking and could classify an average accuracy of 74.3 percent.

Index Terms—Attention state estimation, cognitive rehabilitation with cooking, human support system, machine learning.

Sho Ooi, Kazuki Hashimoto, and Haruo Noma are with Ritsumeikan University, Shiga, Japan (e-mail: SHO.OOI@outlook.jp, khashimoto@mxdlab.net, hanoma@fc.ritsumei.ac.jp).
Mutsuo Sano is with Osaka Institute of Technology, Osaka, Japan (e-mail: Mutsuo.sano@oit.ac.jp).

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Cite: Sho Ooi, Kazuki Hashimoto, Haruo Noma, and Mutsuo Sano, "Research on Dynamic Attention State during Cognitive Rehabilitation with Cooking for Patients with Acquired Brain Injury," International Journal of Machine Learning and Computing vol. 12, no. 3, pp. 102-106, 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijmlc@ejournal.net


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