{"created":"2025-01-19T01:44:27.780521+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00240331","sets":["6164:6165:6640:11802"]},"path":["11802"],"owner":"44499","recid":"240331","title":["屋内滞在データへの大規模言語モデルによる意味付け"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-06-19"},"_buckets":{"deposit":"610533d9-e9ed-4e83-bc77-573feb456324"},"_deposit":{"id":"240331","pid":{"type":"depid","value":"240331","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"屋内滞在データへの大規模言語モデルによる意味付け","author_link":["659126","659128","659125","659127"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"屋内滞在データへの大規模言語モデルによる意味付け"},{"subitem_title":"Adding Semantics to Indoor Staying Data Using Large Language Models","subitem_title_language":"en"}]},"item_type_id":"18","publish_date":"2024-06-19","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":"九州工業大学大学院"},{"subitem_text_value":"九州工業大学大学院"},{"subitem_text_value":"九州工業大学大学院"},{"subitem_text_value":"九州工業大学大学院"}]},"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/240331/files/IPSJ-DICOMO2024207.pdf","label":"IPSJ-DICOMO2024207.pdf"},"date":[{"dateType":"Available","dateValue":"2026-06-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DICOMO2024207.pdf","filesize":[{"value":"4.7 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":"8fc02b5a-5c43-44ea-90c2-ef742ef3e3ce","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"宗元, 竜也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Nazmun, Nahid"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Iqbal, Hassan"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"井上, 創造"}],"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":"本研究では，動画，コンテキストデータから大規模言語モデル（LLM）を使用して自動的に行動ログを作成するシステムを提案する．高齢化が進む現在，介護職員の負担を軽減し，ケアの質を向上させるための解決策が求められる．本研究では介護記録の自動化により効率性の向上に焦点を当てる．本研究では，動画から得られた入退室情報と被験者の交友関係や日常生活に関するコンテキストデータをLLMに入力し，行動記録を生成する手法を提案する．この提案する手法は実験に参加する被験者と介護施設の利用者の入退室のパターンの類似性を考慮し，入退室記録を生成するものである. 入退室のデータは動画で収集し，これらの録画映像からの入退室データとコンテキストデータをLLMに入力して入退室記録を自動生成させた．その結果LLMを使用した記録の精度は71%から79%に向上した．特に，識別可能なコンテキストデータが多い参加者ではモデルの性能が顕著に向上した．本研究はLLMを活用した介護記録の自動化の実現可能性を示しており，介護記録の効率性を大幅に向上させ，最終的には高齢者ケアの質を向上させる可能性があることを示唆している．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1552","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2024論文集"}],"bibliographicPageStart":"1545","bibliographicIssueDates":{"bibliographicIssueDate":"2024-06-19","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2024"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":240331,"updated":"2025-01-19T08:00:46.708313+00:00","links":{}}