{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00217980","sets":["1164:2836:10841:10911"]},"path":["10911"],"owner":"44499","recid":"217980","title":["三次元点群データに基づく着座姿勢推定手法の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-05-19"},"_buckets":{"deposit":"e00b1c15-3e41-403a-a681-e2ca25e0d00f"},"_deposit":{"id":"217980","pid":{"type":"depid","value":"217980","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"三次元点群データに基づく着座姿勢推定手法の提案","author_link":["565816","565814","565813","565815"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"三次元点群データに基づく着座姿勢推定手法の提案"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"空間情報処理","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-05-19","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"大阪大学大学院情報科学研究科"},{"subitem_text_value":"大阪大学大学院情報科学研究科"},{"subitem_text_value":"大阪大学大学院情報科学研究科"},{"subitem_text_value":"大阪大学大学院情報科学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","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/217980/files/IPSJ-DPS22191040.pdf","label":"IPSJ-DPS22191040.pdf"},"date":[{"dateType":"Available","dateValue":"2024-05-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DPS22191040.pdf","filesize":[{"value":"8.4 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":"34"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"0a2f522f-f206-4f94-9cd1-336673912414","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"片山, ひかる"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"水本, 旭洋"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hamada, Rizk"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山口, 弘純"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10116224","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8906","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"デスクワークに従事する労働者が増加している現代において，仕事中の着座時間や姿勢を把握することは，健康維持のために重要である．しかし，既存の技術はカメラや椅子に取り付けるセンサに依存しており，プライバシの侵害や，使用する環境が限られる等の問題がある．そこで本稿では，携帯可能な卓上設置型のセンサデバイスを用いて，カメラ画像や場所に依存せずに，着座姿勢を推定可能な手法を提案する．提案手法では，小型の LiDAR センサを用いて着座姿勢の 3 次元点群データを計測し，計測された 3 次元点群データから抽出された特徴量を入力とする深層学習モデルにより着座姿勢を推定する．卓上の 3 ヶ所にセンサデバイスを設置し収集した 3 次元点群データを用いて，着座推定モデルを構築した結果，F 値 0.87 以上の精度で 9 種類の異なる着座姿勢を推定できることを確認した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告マルチメディア通信と分散処理（DPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-05-19","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"40","bibliographicVolumeNumber":"2022-DPS-191"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":217980,"updated":"2025-01-19T15:19:02.698148+00:00","links":{},"created":"2025-01-19T01:18:24.149878+00:00"}