{"id":207353,"updated":"2025-01-19T19:10:18.617450+00:00","links":{},"created":"2025-01-19T01:09:04.239921+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00207353","sets":["581:10023:10033"]},"path":["10033"],"owner":"44499","recid":"207353","title":["社交ダンスの動作特性を考慮したマルチモーダルセンサによるダンスフィガー認識"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-10-15"},"_buckets":{"deposit":"ce75b108-43aa-4054-be7b-3b239b3f7262"},"_deposit":{"id":"207353","pid":{"type":"depid","value":"207353","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"社交ダンスの動作特性を考慮したマルチモーダルセンサによるダンスフィガー認識","author_link":["517424","517425","517429","517427","517428","517420","517421","517426","517423","517418","517422","517419"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"社交ダンスの動作特性を考慮したマルチモーダルセンサによるダンスフィガー認識"},{"subitem_title":"Ballroom Dance Figure Recognition Using Multi-modal Sensors Considering Dance Motion Characteristics","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集:ユビキタスコンピューティングシステム(Ⅸ)] 行動認識,機械学習,データ収集,社交ダンス,ユビキタスコンピューティング","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2020-10-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋大学大学院工学研究科情報・通信工学専攻"},{"subitem_text_value":"名古屋大学大学院工学研究科情報・通信工学専攻"},{"subitem_text_value":"名古屋大学大学院工学研究科情報・通信工学専攻/現在,京都大学防災研究所"},{"subitem_text_value":"愛知工業大学情報科学部情報科学科"},{"subitem_text_value":"名古屋大学大学院工学研究科情報・通信工学専攻"},{"subitem_text_value":"名古屋大学大学院工学研究科情報・通信工学専攻"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Engineering, Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Nagoya University / Presently with Disaster Prevention Research Institute, Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Depertment of Information Science, Aichi Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Nagoya University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/207353/files/IPSJ-JNL6110005.pdf","label":"IPSJ-JNL6110005.pdf"},"date":[{"dateType":"Available","dateValue":"2022-10-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6110005.pdf","filesize":[{"value":"3.5 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":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"690a6717-dfb2-4288-8990-f0cfa140a628","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"松山, 仁"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"浦野, 健太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"廣井, 慧"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"梶, 克彦"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"米澤, 拓郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"河口, 信夫"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hitoshi, Matsuyama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kenta, Urano","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kei, Hiroi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Katsuhiko, Kaji","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuro, Yonezawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Nobuo, Kawaguchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究では社交ダンスを対象とし,加速度・角速度,視覚のマルチモーダルセンサを用いたダンスフィガー分類手法を提案する.社交ダンスにおける基本技術であるフィガーは,その種類の多さや複雑さゆえ初心者や中級者にとって練習が困難である.フィガーの自動認識によりユーザは自身の踊りの客観的な把握が可能となるため,ダンスフィガーの学習支援が可能となると考えられる.一方で,社交ダンスは2人1組で多彩な動きを行うため,既存の行動認識手法をそのまま適用することは難しい.本稿では,社交ダンスの動作特性を考慮し,フィガーの複雑さや遮蔽などの課題を解決したダンスフィガー認識手法を実現した.本研究では一般的な行動認識手法をベースライン手法として実装したうえで,社交ダンスの姿勢や動作特性を考慮した特徴量を設計・利用した手法を提案・実現し,両者の評価を行った.結果,提案手法の認識精度はF値0.97となり,ベースライン手法を全体で0.06上回った.特にフィガー別の分類精度では,最大で0.6の精度向上を達成した.さらに提案手法が遮蔽物に対しても頑健さを有することを示した.","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"The paper presents a ballroom dance figure classification method by extracting motion characteristics of dances using vision sensor, accelerometer, and gyroscope. It is difficult for intermediate students to practice figures, as they have tremendous types and movements. Thus enabling automatic recognition of the figure will help their exercises by letting dancers grasp themselves objectively. On the other hand, it is difficult to apply the existing activity recognition method as ballroom dance performance has a variety of movements in pairs of dancers. In this paper, we developed the dance figure recognition method robust to complex movement and occlusion by considering the characteristics of the dance. In this study, we first implement a general activity recognition method as a baseline method, then we proposed and realized a method of feature extraction utilizes the posture and motion characteristics of ballroom dance and evaluated both methods. As a result, we showed that the proposed method is 0.06 better than the baseline method in the F1 score and has robustness against occlusions.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1604","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"1591","bibliographicIssueDates":{"bibliographicIssueDate":"2020-10-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"61"}]},"relation_version_is_last":true,"item_2_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20729/00207251","subitem_identifier_reg_type":"JaLC"}]},"weko_creator_id":"44499"}}