WEKO3
アイテム
Recognizing and Understanding Nursing Activities for a Whole Day with a Big Dataset
http://hdl.handle.net/10228/00006370
http://hdl.handle.net/10228/000063707c3d12a9-1296-4410-9786-7618186921bd
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
jip_24_6_853.pdf (1.8 MB)
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| アイテムタイプ | 学術雑誌論文 = Journal Article(1) | |||||||||||||
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| 公開日 | 2017-09-06 | |||||||||||||
| 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 | Recognizing and Understanding Nursing Activities for a Whole Day with a Big Dataset | |||||||||||||
| 言語 | en | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 著者 |
井上, 創造
× 井上, 創造
WEKO
27425
× Ueda, Naonori× Nohara, Yasunobu× Nakashima, Naoki |
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | In this paper, we 1) provide a real nursing data set for mobile activity recognition that can be used for supervised machine learning, 2) provide big data combined with the patient medical records and sensors attempted for 2 years, and also 3) propose a method for recognizing activities for a whole day utilizing prior knowledge about the activity segments in a day. Furthermore, we demonstrate data mining by applying our method to the bigger data with additional hospital data. In the proposed method, we 1) convert a set of segment timestamps into a prior probability of the activity segment by exploiting the concept of importance sampling, 2) obtain the likelihood of traditional recognition methods for each local time window within the segment range, and, 3) apply Bayesian estimation by marginalizing the conditional probability of estimating the activities for the segment samples. By evaluating with the dataset, the proposed method outperformed the traditional method without using the prior knowledge by 25.81% at maximum by a balanced classification rate, and outperformed by 6.5% the F-measure with accepting 1 hour of margin. Moreover, the proposed method significantly reduces duration errors of activity segments from 324.2 seconds of the traditional method to 74.6 seconds at maximum. We also demonstrate the data mining by applying our method to bigger data in a hospital. | |||||||||||||
| 言語 | en | |||||||||||||
| 書誌情報 |
en : Journal of Information Processing 巻 24, 号 6, p. 853-866, 発行日 2016-11-15 |
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| 出版社 | ||||||||||||||
| 出版者 | 情報処理学会 | |||||||||||||
| 言語 | ja | |||||||||||||
| DOI | ||||||||||||||
| 関連タイプ | isIdenticalTo | |||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.2197/ipsjjip.24.853 | |||||||||||||
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| 関連タイプ | isIdenticalTo | |||||||||||||
| 識別子タイプ | URI | |||||||||||||
| 関連識別子 | https://cir.nii.ac.jp/crid/1390282680270544000 | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 1882-6652 | |||||||||||||
| 著作権関連情報 | ||||||||||||||
| 権利情報 | Copyright (c) 2016 by the Information Processing Society of Japan | |||||||||||||
| 著作権関連情報 | ||||||||||||||
| 権利情報 | ここに掲載した著作物の利用に関する注意 本著作物の著作権は情報処理学会に帰属します。本著作物は著作権者である情報処理学会の許可のもとに掲載するものです。ご利用に当たっては「著作権法」ならびに「情報処理学会倫理綱領」に従うことをお願いいたします。 Notice for the use of this material The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). This material is published on this web site with the agreement of the author (s) and the IPSJ. Please be complied with Copyright Law of Japan and the Code of Ethics of the IPSJ if any users wish to reproduce, make derivative work, distribute or make available to the public any part or whole thereof. All Rights Reserved, Copyright (C) Information Processing Society of Japan. | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | mobile activity recognition | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | nursing activity | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | bigdata mining | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | VoR | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||
| 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 | 10308528 | |||||||||||||
| 連携ID | ||||||||||||||
| 値 | 6316 | |||||||||||||
