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  1. 学術雑誌論文
  2. 5 技術(工学)

Modeling Reminder System for Dementia by Reinforcement Learning

http://hdl.handle.net/10228/00009240
http://hdl.handle.net/10228/00009240
c06465ed-dab5-40df-8a77-6386dab643ab
名前 / ファイル ライセンス アクション
LaSEINE-2021_062.pdf LaSEINE-2021_062.pdf (422.6 kB)
アイテムタイプ 学術雑誌論文 = Journal Article(1)
公開日 2023-05-08
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
タイトル
タイトル Modeling Reminder System for Dementia by Reinforcement Learning
言語 en
言語
言語 eng
著者 Fikry, Muhammad

× Fikry, Muhammad

WEKO 35392

en Fikry, Muhammad

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Mairittha, Nattaya

× Mairittha, Nattaya

WEKO 35393

en Mairittha, Nattaya

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井上, 創造

× 井上, 創造

WEKO 27425
e-Rad_Researcher 90346825
Scopus著者ID 9335840200
九工大研究者情報 140

en Inoue, Sozo

ja 井上, 創造

ja-Kana イノウエ, ソウゾウ


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抄録
内容記述タイプ Abstract
内容記述 Prospective memory refers to preparing, remembering and recalling plans that have been conceived in an intended manner. Various busyness and distractions can make people forget the activities that must be done the next time, especially for people with cognitive memory problems such as dementia. In this paper, we propose a reminder system with the idea of taking time and response into consideration to assist in remembering activities. Using the reinforcement learning method, this idea predicts the right time to remind users through notifications on smartphones. The notification delivery time will be adjusted to the user’s response history, which becomes feedback at any available time. Thus, users will get notifications based on the ideal time for each individual either, either with repetition or without repetition, so as not to miss the planned activity. By evaluating the dataset, the results show that our proposed modelling is able to optimize the time to send notifications. The eight alternative times to send notifications can be optimized to get the best time to notify the user with dementia. This implies that our algorithm propose can adjust to individual personality characteristics, which might be a stumbling block in dementia patient care, and solve multi-routine plan problems. Our propose can be useful for users with dementia because we can remind very well that the execution time of notifications is right on target, so it can prevent users with dementia from stressing out over a lot of notifications, but those who miss notifications can receive them back at a later time step, with the result that information on activities to be completed is still available.
言語 en
備考
内容記述タイプ Other
内容記述 3rd International Conference on Activity and Behavior Computing, ABC 2021, 22 October 2021 through 23 October 2021, Online
書誌情報 Smart Innovation, Systems and Technologies

巻 291, p. 149-166, 発行日 2022-05-04
出版社
出版者 Springer
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1007/978-981-19-0361-8_9
ISBN
識別子タイプ ISBN
関連識別子 978-981-19-0360-1
ISBN
識別子タイプ ISBN
関連識別子 978-981-19-0361-8
日本十進分類法
主題Scheme NDC
主題 548
著作権関連情報
権利情報 Copyright (c) 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
出版タイプ
出版タイプ AM
出版タイプResource http://purl.org/coar/version/c_ab4af688f83e57aa
査読の有無
値 yes
研究者情報
URL https://hyokadb02.jimu.kyutech.ac.jp/html/140_ja.html
論文ID(連携)
値 10403090
連携ID
値 10582
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