{"updated":"2025-01-19T10:17:59.447636+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00232853","sets":["934:989:11507:11508"]},"path":["11508"],"owner":"44499","recid":"232853","title":["実評価の不確実性を考慮した加速度センサによる演武競技評価システム"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-02-28"},"_buckets":{"deposit":"0216761a-d286-4af9-85ed-fd35eda0e93c"},"_deposit":{"id":"232853","pid":{"type":"depid","value":"232853","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"実評価の不確実性を考慮した加速度センサによる演武競技評価システム","author_link":["631272","631276","631273","631274","631269","631278","631268","631275","631270","631279","631271","631277"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"実評価の不確実性を考慮した加速度センサによる演武競技評価システム"},{"subitem_title":"Evaluation System for Martial Arts Demonstration Using Acceleration Sensor Considering Uncertainty of Actual Evaluation","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[オリジナル論文] 加速度センサ，機械学習，演武競技，ラベルノイズ，スマートフォンセンサ","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2024-02-28","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋工業大学／現在，株式会社博報堂DYメディアパートナーズ"},{"subitem_text_value":"名古屋工業大学"},{"subitem_text_value":"名古屋工業大学"},{"subitem_text_value":"名古屋工業大学"},{"subitem_text_value":"中部大学"},{"subitem_text_value":"名古屋工業大学"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nagoya Institute of Technology / Presently with Hakuhodo DY Media Partners Incorporated","subitem_text_language":"en"},{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Chubu University","subitem_text_language":"en"},{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/232853/files/IPSJ-TOM1701004.pdf","label":"IPSJ-TOM1701004.pdf"},"date":[{"dateType":"Available","dateValue":"2026-02-28"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOM1701004.pdf","filesize":[{"value":"4.4 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":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"2bc42abc-8409-4ca5-a1a5-ce0f55e35e1d","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"山中, 祥平"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"武藤, 敦子"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"島, 孔介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"森山, 甲一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松井, 藤五郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"犬塚, 信博"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Shohei, Yamanaka","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Atsuko, Mutoh","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kousuke, Shima","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Koichi, Moriyama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tohgoroh, Matsui","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Nobuhiro, Inuzuka","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464803","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_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7780","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年のスマートフォンの普及により一般の人がKinectなどの特別な装置を用いずとも動作解析が可能となってきている．これまでに，演武競技において，演武者の腰に取り付けたスマートフォンの加速度情報から得られる特徴量から決定木学習を用いて評価するシステムが提案されているがその予測精度は高くない．その要因として，特徴量の選択方法や演武競技の実評価の方法に沿っていないことが考えられる．複数の動作からなる演武競技の実評価では，評価者は動作ごとの評価をまとめて感覚的に1つの総合評価を導き出しているため，各動作と総合評価にそのまま機械学習を適用するには不適切である．提案モデルでは，機械学習にラベルノイズを前提とした手法を用いて各動作に対して出力された評価の多数決で総合評価を算出するより人間の評価方法に近いモデルを実現し，実験において評価者の評価を高い精度で再現できることを確認した．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"The recent proliferation of smartphones has made it possible for the common user to analyze movements without the use of special devices such as Kinect. A system that uses decision tree learning to evaluate the performance of a martial arts demonstration based on features obtained from the acceleration information of a smartphone attached to the performer's waist has been proposed, but the prediction accuracy of the system is not high. The reasons for this may be that the feature selection method is not appropriate and is not in line with the actual evaluation method of martial arts demonstrations. In the actual evaluation of martial arts demonstrations, which consist of multiple movements, the evaluator intuitively derives a single overall evaluation by summarizing the evaluations for each movements, so it is inappropriate to apply machine learning directly to each movement and the overall evaluation. In the proposed model, we realized a model that is closer to the human evaluation method by using machine learning on noisy labels to calculate the overall evaluation based on the majority vote of each movement evaluation, and confirmed that it can reproduce the evaluator's evaluation with high accuracy in experiments.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"28","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌数理モデル化と応用（TOM）"}],"bibliographicPageStart":"23","bibliographicIssueDates":{"bibliographicIssueDate":"2024-02-28","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"17"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:33:57.793014+00:00","id":232853,"links":{}}