{"created":"2025-01-19T01:21:16.005243+00:00","updated":"2025-01-19T14:14:01.329718+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00221258","sets":["6504:11035:11042"]},"path":["11042"],"owner":"44499","recid":"221258","title":["Mobile-aware Convolutional Neural Network for Sensor-based Human Activity Recognition"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-02-17"},"_buckets":{"deposit":"090507a2-a168-43ec-a3f2-0beb97c61fe5"},"_deposit":{"id":"221258","pid":{"type":"depid","value":"221258","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Mobile-aware Convolutional Neural Network for Sensor-based Human Activity Recognition","author_link":["579178","579177"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Mobile-aware Convolutional Neural Network for Sensor-based Human Activity Recognition"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2022-02-17","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"福井大"},{"subitem_text_value":"福井大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/221258/files/IPSJ-Z84-4Y-05.pdf","label":"IPSJ-Z84-4Y-05.pdf"},"date":[{"dateType":"Available","dateValue":"2022-10-22"}],"format":"application/pdf","filename":"IPSJ-Z84-4Y-05.pdf","filesize":[{"value":"325.4 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"f3a72855-26ad-435e-848d-85c120a8f028","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"小林, 慧"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"長谷川, 達人"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"センサベースの行動認識では畳み込み層を3層ほど重ねたシンプルなConvolutional Neural Networkが多く用いられている. より高度な構造も研究されているものの,画像認識分野で5年程度前に提案された構造の適用に留まっており,最新のモデル構造の有効性は定かではない. また,オンデバイス推論にはスマートフォン上で高速に動作する深層学習モデルが必要である. 本研究では,画像認識分野で提案されたモダンな構造が行動認識において有効であるか,推定精度やデバイス上での推論時間などの観点から評価する. それを踏まえ,ベイズ最適化によるNeural Architecture Searchによりセンサベースの行動認識に特化したモデル構造を探索し有効性を評価する.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"170","bibliographic_titles":[{"bibliographic_title":"第84回全国大会講演論文集"}],"bibliographicPageStart":"169","bibliographicIssueDates":{"bibliographicIssueDate":"2022-02-17","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2022"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":221258,"links":{}}