{"updated":"2025-01-20T05:26:39.636937+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00177498","sets":["581:8997:8999"]},"path":["8999"],"owner":"11","recid":"177498","title":["測域センサにより取得される歩行パターンを利用した高齢者/若年者弁別手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-02-15"},"_buckets":{"deposit":"ead7dc3b-3e0e-44c0-8887-bfb88dbfa573"},"_deposit":{"id":"177498","pid":{"type":"depid","value":"177498","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"測域センサにより取得される歩行パターンを利用した高齢者/若年者弁別手法","author_link":["376786","376793","376785","376790","376792","376787","376791","376782","376789","376784","376788","376783"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"測域センサにより取得される歩行パターンを利用した高齢者/若年者弁別手法"},{"subitem_title":"A Classification Method of Elderly and Young People Using Walking Pattern Obtained from a Laser Range Scanner","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集：ネットワークサービスと分散処理(特選論文)] サービスロボット，LRF，歩容解析，年齢分類，サポートベクターマシン","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2017-02-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京女子大学現代教養学部数理科学科／現在，電気通信大学"},{"subitem_text_value":"東京女子大学現代教養学部数理科学科／現在，東芝ソリューション株式会社"},{"subitem_text_value":"芝浦工業大学大学院理工学研究科"},{"subitem_text_value":"芝浦工業大学大学院理工学研究科／現在，パラマウントベッド株式会社"},{"subitem_text_value":"芝浦工業大学大学院理工学研究科"},{"subitem_text_value":"東京女子大学現代教養学部数理科学科"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Tokyo Woman's Christian University / Presently with University of Elctro-Communications","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Woman's Christian University / Presently with Toshiba Solutions Corp.","subitem_text_language":"en"},{"subitem_text_value":"Sibaura Institue of Technology","subitem_text_language":"en"},{"subitem_text_value":"Sibaura Institue of Technology / Presently with PARAMOUNT BED Co., Ltd.","subitem_text_language":"en"},{"subitem_text_value":"Sibaura Institue of Technology","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Woman's Christian 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/177498/files/IPSJ-JNL5802013.pdf","label":"IPSJ-JNL5802013.pdf"},"date":[{"dateType":"Available","dateValue":"2019-02-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL5802013.pdf","filesize":[{"value":"2.0 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":"87c2933b-d48b-46ec-acec-f1a74eb35fc8","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2017 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"坂井, 栞"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"木村, 純麗"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"池田, 貴政"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"野見山, 大基"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松日楽, 信人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"加藤, 由花"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Shiori, Sakai","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Sumire, Kimura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takamasa, Ikeda","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Daiki, Nomiyama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Nobuto, Matsuhira","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuka, Kato","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":"我々はこれまで，測域センサで測定された各人の位置情報を基に，インタフェースロボットの応答制御を行い，挨拶などのコミュニケーションに利用する研究を進めてきた．ここでは，人の腰の高さに設置したセンサで人までの距離を時系列データとして取得し，ある時刻の人の位置を確率分布として推定する手法を用いてきた．本稿では，この手法を拡張し，測定位置を足首に変えることで，人の位置だけではなく，人属性を合わせて推定する手法を提案する．人属性推定としては，高齢者/若年者の弁別を対象に，機械学習アルゴリズムを用いて予測器を構築する．ここでは，「歩幅」「歩速」「歩行加速度」など7種類の歩行データの平均値と標準偏差を特徴量とすることで，単一の測域センサから取得した歩行データのみで，属性弁別が可能であることを示す．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Until now, we have studied a dialog control method for interface robots based on human location data measured by a laser range scanner as a human-robot interaction scheme. In this method, we have obtained distance data from the sensor at waist level to the target human as time series data, and have estimated the human location at a time as a probability distribution. This paper enhances the scheme and proposes an estimation method of human attributes in addition to their locations by measuring motion data of human legs at the time of walking. As human attributes, we focus on a classification method of elderly and young people, and construct the prediction model by using a machine learning algorithm. In this paper, we also verify that the attribute classification using walking data obtained from a single laser range scanner becomes possible by adopting seven types of features of walking data, such as strides, velocity and acceleration, for the model.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"383","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"375","bibliographicIssueDates":{"bibliographicIssueDate":"2017-02-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"58"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-19T00:47:03.224563+00:00","id":177498,"links":{}}