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Leveraging Large Language Models to Enhance Understanding of Accelerometer Data on Physical Fatigue Detection Question Answering
https://ipsj.ixsq.nii.ac.jp/records/239562
https://ipsj.ixsq.nii.ac.jp/records/2395626b4f4873-6a4b-4ce1-bce6-e2a3bd19a852
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-UBI24083019.pdf (2.3 MB)
2026年9月19日からダウンロード可能です。
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Copyright (c) 2024 by the Information Processing Society of Japan
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| 非会員：¥660, IPSJ:学会員：¥330, UBI:会員：¥0, DLIB:会員：¥0 | ||
| Item type | SIG Technical Reports(1) | |||||||||
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| 公開日 | 2024-09-19 | |||||||||
| タイトル | ||||||||||
| タイトル | Leveraging Large Language Models to Enhance Understanding of Accelerometer Data on Physical Fatigue Detection Question Answering | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Leveraging Large Language Models to Enhance Understanding of Accelerometer Data on Physical Fatigue Detection Question Answering | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | ケアにおける認識と分析 | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||||
| 資源タイプ | technical report | |||||||||
| 著者所属 | ||||||||||
| Graduate School of Life Science And Systems Engineering, Kyushu Institute of Technology／Department of Informatics, Universitas 17 Agustus 1945 Surabaya | ||||||||||
| 著者所属 | ||||||||||
| Graduate School of Life Science And Systems Engineering, Kyushu Institute of Technology | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Life Science And Systems Engineering, Kyushu Institute of Technology / Department of Informatics, Universitas 17 Agustus 1945 Surabaya | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Life Science And Systems Engineering, Kyushu Institute of Technology | ||||||||||
| 著者名 |
Elsen, Ronando
× Elsen, Ronando
× Sozo, Inoue
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| 著者名(英) |
Elsen, Ronando
× Elsen, Ronando
× Sozo, Inoue
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | 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. | |||||||||
| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | 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. | |||||||||
| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AA11838947 | |||||||||
| 書誌情報 |
研究報告ユビキタスコンピューティングシステム（UBI） 巻 2024-UBI-83, 号 19, p. 1-8, 発行日 2024-09-19 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 2188-8698 | |||||||||
| Notice | ||||||||||
| SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||||
| 出版者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
