{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00213021","sets":["6164:6165:6640:10712"]},"path":["10712"],"owner":"44499","recid":"213021","title":["加速度の時空間情報を考慮した進行方向推定手法の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-06-23"},"_buckets":{"deposit":"7aed4cc0-6fcd-4787-a1ce-5947ef7c895e"},"_deposit":{"id":"213021","pid":{"type":"depid","value":"213021","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"加速度の時空間情報を考慮した進行方向推定手法の検討","author_link":["544338","544341","544337","544339","544340"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"加速度の時空間情報を考慮した進行方向推定手法の検討"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"位置情報システム","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2021-06-23","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_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"},{"subitem_text_value":"名古屋大学大学院工学研究科"},{"subitem_text_value":"国立情報学研究所／名古屋大学未来社会創造機構"},{"subitem_text_value":"名古屋大学大学院工学研究科"},{"subitem_text_value":"名古屋大学大学院工学研究科／名古屋大学未来社会創造機構"}]},"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/213021/files/IPSJ-DICOMO2021123.pdf","label":"IPSJ-DICOMO2021123.pdf"},"date":[{"dateType":"Available","dateValue":"2023-06-23"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DICOMO2021123.pdf","filesize":[{"value":"2.6 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":"44"}],"accessrole":"open_date","version_id":"c7dba6f9-d244-46ea-818a-c50214e9813d","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"吉田, 拓人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"浦野, 健太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"青木, 俊介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"米澤, 拓郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"河口, 信夫"}],"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_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本稿では，スマートフォンを用いた PDR　(Pedestrian Dead Reckoning)　のための進行方向推定手法 について検討を行う．スマートフォンの頻繁な端末姿勢の変化に対応するため，進行方向推定手法にはセ ンサ姿勢の変化に対する頑健性が必須となる.そのため，加速度平面成分を用いてセンサ姿勢の変化によ らず進行方向を推定できる PCA (Principal Component Analysis) ベースの手法が注目されてきた．しかし，我々は加速度平面成分の空間情報のみでは，歩行者が取り得るあらゆる歩容パターンの認識は困難で あると考える．そこで我々は歩容に対する頑健性の向上を目的とし，加速度平面成分の時空間情報を用い て進行方向を推定する NN (Neural Network) ベースの手法を提案する．NN の学習と評価に用いるデー タはスマートフォンと測量機器の TOPCON GT1205 を用いて収集する．NN のアーキテクチャとして， CNN (Convolutional Neural Network) と BiLSTM (Bidirectional Long Short-Term Memory) をベースとした 2 つを用意し，評価を行う．評価の結果，CNN が総合的に最も推定精度が高く，歩容に対する頑健性も高いことがわかった．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"899","bibliographic_titles":[{"bibliographic_title":"マルチメディア，分散協調とモバイルシンポジウム2021論文集"}],"bibliographicPageStart":"893","bibliographicIssueDates":{"bibliographicIssueDate":"2021-06-23","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":213021,"updated":"2025-01-19T17:18:05.473819+00:00","links":{},"created":"2025-01-19T01:13:56.304089+00:00"}