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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/2237/000284548917825f-de04-4ed1-9204-8387588c582a
名前 / ファイル | ライセンス | アクション |
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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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× NISHIDA, Masafumi× KITAOKA, Norihide× TODA, Tomoki× TAKEDA, Kazuya |
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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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著者版フラグ | ||||||
値 | publisher |