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  1. I500 未来社会創造機構
  2. I500a 雑誌掲載論文
  3. 学術雑誌

Daily Activity Recognition with Large-Scaled Real-Life Recording Datasets Based on Deep Neural Network Using Multi-Modal Signals

http://hdl.handle.net/2237/00028454
http://hdl.handle.net/2237/00028454
8917825f-de04-4ed1-9204-8387588c582a
名前 / ファイル ライセンス アクション
e101-a_1_199.pdf e101-a_1_199 (4.2 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2018-08-08
タイトル
タイトル Daily Activity Recognition with Large-Scaled Real-Life Recording Datasets Based on Deep Neural Network Using Multi-Modal Signals
言語 en
著者 HAYASHI, Tomoki

× HAYASHI, Tomoki

WEKO 78790

en HAYASHI, Tomoki

Search repository
NISHIDA, Masafumi

× NISHIDA, Masafumi

WEKO 78791

en NISHIDA, Masafumi

Search repository
KITAOKA, Norihide

× KITAOKA, Norihide

WEKO 78792

en KITAOKA, Norihide

Search repository
TODA, Tomoki

× TODA, Tomoki

WEKO 78793

en TODA, Tomoki

Search repository
TAKEDA, Kazuya

× TAKEDA, Kazuya

WEKO 78794

en TAKEDA, Kazuya

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
権利
言語 en
権利情報 copyright(c)2018 IEICE
キーワード
主題Scheme Other
主題 human activity recognition
キーワード
主題Scheme Other
主題 activity of daily living
キーワード
主題Scheme Other
主題 database
キーワード
主題Scheme Other
主題 deep neural networks
キーワード
主題Scheme Other
主題 adaptation
抄録
内容記述 In this study, toward the development of smartphone-based monitoring system for life logging, we collect over 1,400 hours of data by recording including both the outdoor and indoor daily activities of 19 subjects, under practical conditions with a smartphone and a small camera. We then construct a huge human activity database which consists of an environmental sound signal, triaxial acceleration signals and manually annotated activity tags. Using our constructed database, we evaluate the activity recognition performance of deep neural networks (DNNs), which have achieved great performance in various fields, and apply DNN-based adaptation techniques to improve the performance with only a small amount of subject-specific training data. We experimentally demonstrate that; 1) the use of multi-modal signal, including environmental sound and triaxial acceleration signals with a DNN is effective for the improvement of activity recognition performance, 2) the DNN can discriminate specified activities from a mixture of ambiguous activities, and 3) DNN-based adaptation methods are effective even if only a small amount of subject-specific training data is available.
言語 en
内容記述タイプ Abstract
出版者
言語 ja
出版者 一般社団法人電子情報通信学会
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1587/transfun.E101.A.199
関連情報
関連タイプ isVersionOf
識別子タイプ URI
関連識別子 http://search.ieice.org/index.html
ISSN
収録物識別子タイプ PISSN
収録物識別子 0916-8508
書誌情報 en : IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

巻 E101A, 号 1, p. 199-210, 発行日 2018-01-01
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