{"created":"2025-01-19T01:43:04.409298+00:00","updated":"2025-01-19T08:17:00.167920+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00239430","sets":["1164:8228:11483:11716"]},"path":["11716"],"owner":"44499","recid":"239430","title":["Leveraging Large Language Models to Enhance Understanding of Accelerometer Data on Physical Fatigue Detection Question Answering"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-09-19"},"_buckets":{"deposit":"1870c151-6d79-4782-99f7-f94c331cf875"},"_deposit":{"id":"239430","pid":{"type":"depid","value":"239430","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Leveraging Large Language Models to Enhance Understanding of Accelerometer Data on Physical Fatigue Detection Question Answering","author_link":["656304","656302","656303","656301"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Leveraging Large Language Models to Enhance Understanding of Accelerometer Data on Physical Fatigue Detection Question Answering"},{"subitem_title":"Leveraging Large Language Models to Enhance Understanding of Accelerometer Data on Physical Fatigue Detection Question Answering","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ケアにおける認識と分析","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-09-19","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Life Science And Systems Engineering, Kyushu Institute of Technology/Department of Informatics, Universitas 17 Agustus 1945 Surabaya"},{"subitem_text_value":"Graduate School of Life Science And Systems Engineering, Kyushu Institute of Technology"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Life Science And Systems Engineering, Kyushu Institute of Technology / Department of Informatics, Universitas 17 Agustus 1945 Surabaya","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Life Science And Systems Engineering, Kyushu Institute of Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/239430/files/IPSJ-ASD24030019.pdf","label":"IPSJ-ASD24030019.pdf"},"date":[{"dateType":"Available","dateValue":"2026-09-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-ASD24030019.pdf","filesize":[{"value":"2.3 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":"49"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"386e4dc3-bd3b-40b7-a1ed-022ebef5d797","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Elsen, Ronando"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Sozo, Inoue"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Elsen, Ronando","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Sozo, Inoue","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA1271737X","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":"2189-4450","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"In this paper, we enhance the ability of Large Language Models (LLMs) to interpret accelerometer data for detecting physical fatigue. While sensors are increasingly used to monitor physical conditions, analyzing their data remains challenging. We focus on improving LLMs' question-answering capabilities, specifically for data generated from physical activities. To address the complexities of sensor data, we introduce and compare two input formats―list-based and graph-based representations. Using models like gpt-3.5-turbo and gpt-4o-mini in both zero-shot and few-shot scenarios, we find that graph-based formats significantly boost LLM performance, achieving a top F1-score of 67.50%. This research highlights the importance of refining data formats and enhancing model capabilities to improve LLMs' effectiveness in health monitoring applications.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper, we enhance the ability of Large Language Models (LLMs) to interpret accelerometer data for detecting physical fatigue. While sensors are increasingly used to monitor physical conditions, analyzing their data remains challenging. We focus on improving LLMs' question-answering capabilities, specifically for data generated from physical activities. To address the complexities of sensor data, we introduce and compare two input formats―list-based and graph-based representations. Using models like gpt-3.5-turbo and gpt-4o-mini in both zero-shot and few-shot scenarios, we find that graph-based formats significantly boost LLM performance, achieving a top F1-score of 67.50%. This research highlights the importance of refining data formats and enhancing model capabilities to improve LLMs' effectiveness in health monitoring applications.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告高齢社会デザイン(ASD)"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-09-19","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"19","bibliographicVolumeNumber":"2024-ASD-30"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":239430,"links":{}}