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加速度データからの機械学習による行動認識
http://hdl.handle.net/10098/10930
http://hdl.handle.net/10098/1093036dd0584-afd2-4fdd-b555-281541026497
名前 / ファイル | ライセンス | アクション |
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bd10122410.pdf (376.3 kB)
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Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||
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公開日 | 2020-04-01 | |||||
タイトル | ||||||
タイトル | 加速度データからの機械学習による行動認識 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | A Method to Human Activity Recognition Using Acceleration Databy Machine Learning | |||||
言語 | ||||||
言語 | jpn | |||||
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 | Machine Learning | |||||
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 | departmental bulletin paper | |||||
著者 |
北村, 和也
× 北村, 和也× 小高, 知宏× 黒岩, 丈介× 諏訪., いずみ× 白井, 治彦× Kotamura, Kazuya× Odaka, Tomohiro× Kuroiwa, Jousuke× Suwa, Izumi× Shirai, Haruhiko |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | In this paper, we proposed a method to identify human behavior using a 3-axis acceleration sensor of a smartphone. To realize context-aware services such as efficient energy-saving appliance controland elderly monitoring, high-accuracy in-home living activity recognition is essential. We tried to improve recognition accuracy by using deep learning for HAR(Human Activity Recognition). The proposed methods are CNN(Convolution Neural Network) and lstm(Long short-term memory)methods. An experiment was performed using the HASC dataset to verify the effectiveness ofthe method. The HASC data set is data of three-axis acceleration. As a result of the experiment, theresult using the CNN method was 95.4%, and the result using the LSTM method was 94.3%. | |||||
書誌情報 |
福井大学 大学院工学研究科 研究報告 巻 68, p. 59-65, 発行日 2020-03 |
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出版者 | ||||||
出版者 | 福井大学 大学院工学研究科 | |||||
ISBN | ||||||
識別子タイプ | ISBN | |||||
関連識別子 | 2433815X | |||||
書誌レコードID | ||||||
識別子タイプ | NCID | |||||
関連識別子 | TD10122410 |