{"updated":"2025-01-19T16:55:00.199354+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00213802","sets":["581:10433:10444"]},"path":["10444"],"owner":"44499","recid":"213802","title":["カメラを用いた顔位置計測による運動識別システム"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-11-15"},"_buckets":{"deposit":"f7cf7999-b4e6-4450-97fb-c7fc2f2c0b77"},"_deposit":{"id":"213802","pid":{"type":"depid","value":"213802","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"カメラを用いた顔位置計測による運動識別システム","author_link":["547482","547484","547481","547483","547486","547485"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"カメラを用いた顔位置計測による運動識別システム"},{"subitem_title":"Exercise Recognition System Using Facial Image Information","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集:エンタテインメントコンピューティング] 運動識別，モバイル端末，機械学習","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2021-11-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"慶應義塾大学"},{"subitem_text_value":"慶應義塾大学"},{"subitem_text_value":"慶應義塾大学"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Keio University","subitem_text_language":"en"},{"subitem_text_value":"Keio University","subitem_text_language":"en"},{"subitem_text_value":"Keio 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/213802/files/IPSJ-JNL6211005.pdf","label":"IPSJ-JNL6211005.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6211005.pdf","filesize":[{"value":"2.2 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":"3a78c264-d569-47d9-9f9b-fac42008667a","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"加藤, 花歩"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"夏, 成碩"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"杉浦, 裕太"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kaho, Kato","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Chengshuo, Xia","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuta, Sugiura","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":"健康を維持するために日常的な運動が重要であるが，運動の継続が困難な人は多い．本稿では手軽な運動管理や運動ゲーム作成のための，モバイル端末の内蔵カメラを利用した顔画像情報による運動識別システムを提案する．本システムはカメラで運動中のユーザの顔画像情報を取得し，運動の種類を識別できる．ユーザがカメラの画角に自身の顔を収めるように運動すると，システムは顔画像情報から顔上の追跡点を抽出する．追跡点より算出した特徴量をサポートベクタマシンで学習することで9種類の運動を識別した．ユーザごとの精度検証の結果，平均識別精度は97.2%となった．加えて，システムの高速化や複数人の同時計測に向けて，最適なウィンドウサイズや次元削減の検討，ユーザとカメラの位置関係による識別への影響の調査を行った．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Daily exercise has played a significant role for people in staying healthy; however, some people cannot do moderate exercise continuously. In this paper, we propose an exercise recognition system using facial image information for making exercise management convenient. The proposed system gets facial image information from a built-in camera on a mobile device and can recognize and classify multiple kinds of exercises. When a user exercises and their face is within the viewing angle, the system extracts features from the facial images. Via an experiment with user's own data, the average classification accuracy reached up to 97.2%. To improve the operation of the designed system, we also evaluated the suitable window size, dimension reduction, and influence of the user's standing position.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1816","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"1806","bibliographicIssueDates":{"bibliographicIssueDate":"2021-11-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"11","bibliographicVolumeNumber":"62"}]},"relation_version_is_last":true,"item_2_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20729/00213694","subitem_identifier_reg_type":"JaLC"}]},"weko_creator_id":"44499"},"created":"2025-01-19T01:14:38.897273+00:00","id":213802,"links":{}}