{"created":"2025-01-19T01:04:22.593304+00:00","updated":"2025-01-19T21:12:08.021523+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00200905","sets":["1164:3027:9664:9908"]},"path":["9908"],"owner":"44499","recid":"200905","title":["Preliminary investigation of using deep reinforcement learning to control a mobile robot for human activity recognition"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-12-03"},"_buckets":{"deposit":"06bed31e-ae33-49a3-88f9-07526f82248d"},"_deposit":{"id":"200905","pid":{"type":"depid","value":"200905","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Preliminary investigation of using deep reinforcement learning to control a mobile robot for human activity recognition","author_link":["489524","489528","489532","489527","489530","489526","489531","489533","489529","489525"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Preliminary investigation of using deep reinforcement learning to control a mobile robot for human activity recognition"},{"subitem_title":"Preliminary investigation of using deep reinforcement learning to control a mobile robot for human activity recognition","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"予測と認識","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2019-12-03","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Araya Inc."},{"subitem_text_value":"Araya Inc."}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Araya Inc.","subitem_text_language":"en"},{"subitem_text_value":"Araya Inc.","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/200905/files/IPSJ-HCI19185012.pdf","label":"IPSJ-HCI19185012.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-03"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-HCI19185012.pdf","filesize":[{"value":"7.8 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"33"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"a10c144e-3e32-4c00-99c0-4b186f78b49c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Teerawat, Kumrai"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Joseph, Korpela"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuya, Maekawa"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yen, Yu"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryota, Kanai"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Teerawat, Kumrai","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Joseph, Korpela","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuya, Maekawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yen, Yu","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryota, Kanai","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA1221543X","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8760","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Due to recent advances in robotics technologies, it is becoming feasible for mobile robots to use their sensors to observe daily human activities for the purpose of human activity recognition (HAR) in indoor environments. However, when doing so, the robot will have difficultly observing and recognizing human activities when it is positioned behind the human or some obstacle. Therefore, this work investigates a method for using deep reinforcement learning to control the mobile robot's movement when observing human activities. Our objective is to minimize the movement of the robot (i.e., its energy consumption) while maximizing its human activity recognition accuracy. Moreover, our method introduces a new HAR method based on skeletal and visual features extracted from the robot's captured images.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Due to recent advances in robotics technologies, it is becoming feasible for mobile robots to use their sensors to observe daily human activities for the purpose of human activity recognition (HAR) in indoor environments. However, when doing so, the robot will have difficultly observing and recognizing human activities when it is positioned behind the human or some obstacle. Therefore, this work investigates a method for using deep reinforcement learning to control the mobile robot's movement when observing human activities. Our objective is to minimize the movement of the robot (i.e., its energy consumption) while maximizing its human activity recognition accuracy. Moreover, our method introduces a new HAR method based on skeletal and visual features extracted from the robot's captured images.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告ヒューマンコンピュータインタラクション（HCI）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2019-12-03","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"12","bibliographicVolumeNumber":"2019-HCI-185"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":200905,"links":{}}