{"id":197254,"updated":"2025-01-19T22:25:44.812495+00:00","links":{},"created":"2025-01-19T01:01:43.622587+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00197254","sets":["6504:9795:9797"]},"path":["9797"],"owner":"6748","recid":"197254","title":["健常歩行者センサデータからのバリア検出における入出力方法の考察"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-02-28"},"_buckets":{"deposit":"be204106-9eec-4a41-bc55-b5f0a2bb3550"},"_deposit":{"id":"197254","pid":{"type":"depid","value":"197254","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"健常歩行者センサデータからのバリア検出における入出力方法の考察","author_link":["472976"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"健常歩行者センサデータからのバリア検出における入出力方法の考察"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"コンピュータと人間社会","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2019-02-28","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"日大"}]},"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/197254/files/IPSJ-Z81-7K-01.pdf","label":"IPSJ-Z81-7K-01.pdf"},"date":[{"dateType":"Available","dateValue":"2019-05-28"}],"format":"application/pdf","filename":"IPSJ-Z81-7K-01.pdf","filesize":[{"value":"414.1 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"e27855a4-bb55-4017-97e1-2cfff5b89dce","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"宮田, 章裕"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"屋内外には段差・階段などのバリアが多数存在し，障害者や高齢者の円滑な移動を妨げている．既存手法が抱えるバリア情報の精度と網羅性のトレードオフの問題を解決するため，我々は，健常者の歩行時に生じる加速度データをDeep Learningで分析することで，広範囲のバリアの存在・種別を判定する方法を提案してきた．しかし，この方法は入出力に問題を抱えている．入力においては，加速度データの提供に対するモチベーションを高める仕組みが無いため，多くのデータが収集できない可能性がある．出力においては，我々の方法ではバリアの存在・種別の確からしさが確率的に表現されるため，従来の確実な情報を前提としたバリアフリーマップでは適切な表現が行えない可能性がある．本稿では，これらの入出力の問題を整理し，解決のための考察を行う．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"426","bibliographic_titles":[{"bibliographic_title":"第81回全国大会講演論文集"}],"bibliographicPageStart":"425","bibliographicIssueDates":{"bibliographicIssueDate":"2019-02-28","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2019"}]},"relation_version_is_last":true,"weko_creator_id":"6748"}}